tslab-mcp
Enables users to perform deterministic time series forecasting through Claude by running reproducible Python statistical and foundation models, providing tools for loading data, cross-validation, forecasting, anomaly detection, and exporting re-runnable manifests without any LLM involvement.
Caution. Specific findings reduced this grade. They are listed on the page. Grades reflect the full trust pyramid: code, verification depth, and reputation. New projects cap at C until adoption is earned.
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pedrobtz
Source: Glama
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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.
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
8/8 tools missing one or more hints — tsf_load_series (missing: readOnlyHint, destructiveHint, idempotentHint, openWorldHint); tsf_describe_series (missing: readOnlyHint, destructiveHint, idempotentHint, openWorldHint); tsf_list_models (missing: readOnlyHint, destructiveHint, idempotentHint, openWorldHint), +5 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.
Descriptions match behaviour
1 tool describes read intent but its handler mutates — tsf_export_report (line 484: written = artifacts.write_text(content, "report", stem, suffix))
Rename the tool, rewrite the description, or move the side-effect into a separate clearly-named tool.
Tool description accuracy
tsf_export_report: description implies read-only but handler writes/deletes/executes
Update tool descriptions to accurately reflect all capabilities — especially write, delete, or execute operations.
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