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Team Semantic Search

team_semantic_search
Read-onlyIdempotent

Search team entries by meaning using AI embeddings, filter by metadata, or find entries related to a specific entry ID to retrieve relevant project context.

Instructions

Perform semantic/vector search on team entries using AI embeddings. Supports find-related-by-ID (entry_id) and metadata filters. Threshold default is 0.25 (try 0.15-0.2 for broader matches, 0.4+ for strict matches). Entries auto-indexed on creation. Requires TEAM_DB_PATH.

Input Schema

NameRequiredDescriptionDefault
tagsNo
limitNo
queryNo
end_dateNo
entry_idNoFind entries related to this entry ID
pr_numberNoFilter results by GitHub PR number
entry_typeNo
start_dateNo
issue_numberNoFilter results by GitHub issue number
workspace_idNoOptional workspace isolation identifier (e.g. "alpha"). Do not use numeric project IDs or "workspace" as the key.
hint_on_emptyNoInclude hint when no results found (default: true)
project_numberNo
workflow_run_idNoFilter results by GitHub Actions workflow run ID
include_archivedNo
similarity_thresholdNo

Output Schema

NameRequiredDescriptionDefault
codeNoError code (e.g. VALIDATION_ERROR, INTERNAL_ERROR)
hintNo
countNo
errorNoHuman-readable error message
queryNo
detailsNoAdditional error context
entriesNo
entryIdNo
successNo
categoryNoError category (validation, query, connection, internal)
suggestionNoSuggested fix for the error
recoverableNoWhether the error is recoverable
aggregated_errorsNoDetails of individual errors in a multi-step operation

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv9.0.0

TDQS

A4/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already establish readOnly, idempotent, non-destructive, so the safety profile is covered. The description adds real value beyond that: index freshness ('entries auto-indexed on creation') and an environment prerequisite ('requires TEAM_DB_PATH'), both of which affect whether results can be trusted. Return format is not described, but an output schema exists to cover that.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Four dense sentences, front-loaded with the core action, then the two modes, then tuning guidance, then operational prerequisites. No filler and nothing repeated from the schema.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 15-parameter tool with an output schema, the description covers the essentials: what it searches, the two query modes, threshold tuning, index freshness, and the env requirement. The notable gap is that zero parameters are required, so the behavior of a bare call (no query, no entry_id, no filters) is undefined here.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is only 40% across 15 parameters, so the description must carry weight. It does add threshold semantics and the entry_id relationship mode, which go beyond the schema. But tags, entry_type, start_date/end_date, limit, include_archived, project_number, and hint_on_empty receive no explanation in either place.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource: semantic/vector search over team entries using embeddings. This is clearly distinguishable from keyword-style siblings like team_search by the 'semantic/vector' framing. It does not, however, explicitly name which sibling to use instead, so an agent must infer the boundary from the tool name alone.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives genuinely actionable guidance on the similarity_threshold knob (default 0.25, 0.15-0.2 for broader, 0.4+ for strict) and notes the find-related-by-ID mode via entry_id. It stops short of stating when to prefer this over team_search or cross_team_search, so the alternative-selection rule is left implicit.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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