assessment-suite (tikankika/assessment-suite) is an MCP server listed on the M8ven Trust Index. It scores 74 out of 100, grade C. It declares 43 tools. No publisher has claimed this listing.

C
Emerging
74/100

assessment-suite

AI-assisted analytic assessment that keeps the teacher's judgement central. Two Model Context Protocol servers for Claude Desktop; the assessment logic lives in editable methodology documents.

Emerging. No concerning findings. Grades remain capped until the project builds reputation through adoption. Grades reflect the full trust pyramid: code, verification depth, and reputation. New projects cap at C until adoption is earned.

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Who stands behind it

tikankika

Source: github_repo_search

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Install from

The grade above is for the source repository. Registries can serve a different version, so we mark the ones we were not able to read.

// key findings
No credential exfiltration, no sensitive file access, no obfuscation
Static analysis found nothing flowing your secrets to unexpected places.
Open source with a license and README
Anyone can audit the code, the license is declared, and the publisher documents what it does.
// tools this server exposes43 tools

These names and descriptions are the publisher's own, read from the source code. We print them as written. Our assessment is the findings above, not this list.

init
phase6_start
phase6_methodology
phase6_rubric
phase6_read_next
phase6_write
phase6_status
phase6_get
assessment_delete
teacher_annotation
rubric_read
phase6_write_free
process_memo
assessment_purpose
hermeneutic_read
reflect_insights
reflect_uncertainty
reflect_aspect_analysis
phase2b_questions
phase4b_rubric
rubric_edit
phase4c_save
phase2c_boundaries
phase2d_students
phase3_validate
json_write
project_repair

Fix path portability issues in an assessment project (RFC-015). This tool converts absolute paths (e.g., /Users/username/...) to relative paths, enabling projects to be shared across different machines. WHEN TO USE: - Project was created on a different machine/user account - MCP tools fail with "f

project_status

Get comprehensive status of an assessment project (RFC-013: Session Continuity). WHEN TO USE: - Starting work on an existing project - Resuming after a break - Checking progress before Phase 8+ - Finding where to continue assessment WHAT IT RETURNS: - Project overview (name, created, last updated)

phase_start
phase_complete

Complete a phase session and save the final document. Call this when the LLM-driven dialogue is finished and you have the final content. This tool: 1. Removes any draft section from the Complete_ report 2. Writes final phase section to Complete_ report (with markers) 3. Saves standalone file to th

phase6_post_format

Phase 6-post: Assessment Format Detection - REQUIRED step between Phase 6 and Phase 7. Run this AFTER completing Phase 6 assessment and BEFORE Phase 7 report generation. TWO-PHASE WORKFLOW: (1) LOAD mode - Returns Q-files + sample content + methodology (2) SAVE mode - Saves detected format to exam

student_report_update

Update BOTH student report files for Phase 9-12. Updates TWO files simultaneously: 1. complete_assessment/Complete_{student}.md (progressive report) 2. Phase-specific file: - Phase 9: 09_qualitative/Student_*_generalization.md - Phase 10: 10_extrapolation/Student_*_extrapolation.md - Phase

scan_source_directory

Scan directory to identify exam, rubric, and student answer files. FIRST STEP for new projects. After this returns, call initialize_project. All paths must be within the configured workspace directory. DO NOT use bash, view, ls, or Filesystem MCP - use THIS tool instead.

initialize_project

Create project structure with folders and files. SECOND STEP - Call AFTER scan_source_directory. Then call convert_documents to process PDFs. Creates folders, copies files, generates project_state.json. Required even if markdown files already exist.

convert_documents

Convert PDF files to readable markdown text, or copy PDFs as-is. THIRD STEP - Call AFTER initialize_project. This is the ONLY way to convert PDFs. Do NOT use bash, pymupdf4llm, or Python scripts. Converts ALL PDFs in input_dir to markdown files in output_dir. Non-PDF files (.md, .txt) are copied a

extract_student_answers

Extract student answers into Q-files (one file per question). This is the ONLY way to create Q-files. Do NOT use bash, Python scripts, or manual extraction. Prefers Phase 3 annotated files from 03_material/ (marker-based extraction). Falls back to boundary matching from 02_markdown/ if no markers

generate_reports

Generate student reports from assessed Q-files. Modes: preview → analyze → generate (with confirmed=True). Use mode="analyze" when free-text assessments aren't parsing correctly. Input: 06_analytic_assessment/Q*.md → Output: 07_analytic_student/

quantitative_summary

Generate quantitative JSON summaries from student reports. Call AFTER generate_reports. Creates points/percentages per student. Input: 07_analytic_student/ → Output: 08_quantitative/*.json

phase3_prepare

Prepare student files for Phase 3 annotation (RFC-034). Copies student markdown files from 02_markdown/student_answers/ to 03_material/student_answers/, adds <!-- student: {id} --> header and permanent line indices (0001, 0002...) to each file. Call AFTER Phase 2C/2D. Then run phase3_annotate to a

phase3_annotate

Auto-annotate student files with Phase 3 markers (RFC-034). Reads prepared files from 03_material/student_answers/, strips line indices, runs auto-annotation using answer_boundaries from exam_config.yaml, re-indexes, and writes back in-place. Call AFTER phase3_prepare. Then run phase3_validate to

phase3_file_edit

Edit Phase 3 student files: insert/remove annotation markers (RFC-034). Use AFTER inspect_file to read the student file and understand its structure. Insert Phase 3 markers at exact line-index positions. Line indices (0001, 0002...) are PERMANENT — inserts/removes never change them. Workflow: insp

inspect_file

DEBUG: Read file contents. Not for new projects - use scan_source_directory instead.

list_files

DEBUG: List directory contents. Not for new projects - use scan_source_directory instead.

// environment variables
To run this server yourself, you supply these values. They go in your own MCP client configuration and stay on your machine. The secret label means the value is sensitive, not that the server mishandles it.
configMETHODOLOGY_PATH
// quality suggestions

Tool annotations

No tools have read-only/destructive annotations

Add readOnlyHint or destructiveHint annotations to every tool so hosts can warn users before invoking.

All four hints declared on every tool

43/43 tools missing one or more hints — init (missing: readOnlyHint, destructiveHint, idempotentHint, openWorldHint); phase6_start (missing: readOnlyHint, destructiveHint, idempotentHint, openWorldHint); phase6_methodology (missing: readOnlyHint, destructiveHint, idempotentHint, openWorldHint), +40 more. OpenAI's directory rejects tools where any of the four hints are missing or non-boolean.

For every tool, set all four hints (readOnlyHint, destructiveHint, idempotentHint, openWorldHint) to explicit true/false values that match the handler’s actual behaviour.

Tool test coverage

22/43 tools referenced in tests (51%)

Write tests that reference each tool by name so every tool has at least one test.

Claim the listing to review these findings one by one and send us a correction where you disagree, straight to the team. Claiming also means we tell you when the grade moves, and reach you first if we find anything urgent.

// full audit trail
The findings above are the summary. The full trail, every check we ran, each deduction, the network hosts observed and the dependency advisories, goes to verified publishers, along with an alert whenever a new one lands. Verified publishers can also review each finding and dispute it in one click. Publisher corrections have sharpened several of our checks this month, because the maintainer knows the codebase better than any scanner.
// improvement guidance — verified publishers only
We have 3 concrete improvements we can share with the publisher of this MCP. Each comes with specific guidance to raise the trust score.
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commit: 112af998df129bc59bbd72f9055e57f65545c213
code hash: ae8f897c6b41303b697f13ecc8ff5c19cecada03fbc35589b301af8a7b1e238c
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