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reqs_embedding_stats

Check embedding coverage for requirements to reveal how many have vectors, dimension counts, and mixed widths. Detect mixed embedding models that can silently exclude rows from similarity search.

Instructions

Report on embedding coverage --- how many requirements have embeddings.

This tool takes no input at all, so it is not a privacy surface and carries no precondition block of its own; that is said explicitly rather than left as an omission a reader has to interpret.

Beyond coverage it reports dimensions --- which vector widths this tracker actually holds, and how many rows each --- plus mixed, true when there is more than one. That is the channel for noticing a tracker that received vectors from two different embedding models: reqs_search_similar silently excludes rows of a width other than your query's, and being able to see the split here is what keeps that from looking like "nothing is similar". Both keys are always present; an empty dimensions list means no vectors are stored, never that the check did not run.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.2.2

TDQS

A4.7/5.0
Behavior5/5

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

No annotations are present, so the description carries the full burden of behavioral disclosure. It explicitly states the tool takes no input, is not a privacy surface, and has no precondition block. It also explains the always-present keys and the critical interpretation that an empty dimensions list means no vectors are stored, not that the check did not run.

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

Conciseness4/5

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

The description is front-loaded with the core purpose and then provides meaningful detail about output semantics and the use case for diagnosing mixed vector widths. One meta-commentary clause about saying something explicitly rather than leaving it as an omission is unnecessary and slightly verbose, but the overall structure is clear and focused.

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

Completeness5/5

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

For a zero-parameter reporting tool with an output schema available, the description covers the purpose, all key output fields, and the main edge-case interpretation. It explicitly addresses the most likely confusion around empty dimensions and connects the tool's value to reqs_search_similar behavior, making it complete for correct invocation and interpretation.

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

Parameters5/5

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

There are zero parameters, so the schema is trivially complete. The description goes beyond the schema by explicitly stating the tool takes no input and therefore carries no privacy or precondition implications, removing any ambiguity about required context or arguments.

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

Purpose5/5

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

The first sentence states a precise operation: report on embedding coverage, specifically how many requirements have embeddings. It further distinguishes the tool from siblings by describing the additional dimensions and mixed keys and by referencing reqs_search_similar's behavior. Purpose is unambiguous and specific.

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?

The description gives a concrete diagnostic scenario: use this tool to detect a tracker holding vectors from multiple embedding models, especially when reqs_search_similar returns no matches due to silently excluded width mismatches. It clearly explains the no-input nature and lack of preconditions, though it does not explicitly enumerate alternatives or say when not to use it.

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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