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self_test

Read-onlyIdempotent

Service health probe: runs 6 internal checks and reports how many passed. Confirms the server is up and responding - it does NOT probe each tool individually. Use to verify connectivity before production use.

EXAMPLE USER QUERIES THAT MATCH THIS TOOL: user: "Run a health check before I send the broadcast" -> call self_test({})

WHEN TO USE: Use at agent startup, before high-stakes task sequences, or after receiving unexpected errors to check if the service is degraded. WHEN NOT TO USE: Do not call more than once per minute in production. COST: free - no key required LATENCY: ~200ms

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

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

  1. First observed

TDQS

A4.7/5.0
Behavior5/5

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

The description adds substantial behavioral detail beyond the annotations: it runs exactly 6 internal checks, reports a count, does not test individual tools, is free, takes ~200ms, and should not be called more than once per minute. This goes well beyond the readOnly/idempotent/non-destructive hints.

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?

The description is front-loaded with the core purpose, then provides concrete usage examples, when-to-use guidance, cost, latency, and rate-limit restrictions. Each section adds practical value without unnecessary fluff.

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 parameterless health-check tool with strong annotations and no output schema, the description covers everything an agent needs: what it does, what it does not do, when to use it, rate limits, cost, latency, and an example invocation. Nothing essential is missing.

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

Parameters4/5

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

The tool has zero parameters and an empty schema, so there is little for the description to clarify. The description correctly implies the call takes no input by showing 'call self_test({})'. This is the appropriate baseline for a parameterless tool.

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 description clearly identifies a specific verb and resource: 'Service health probe: runs 6 internal checks and reports how many passed.' It explicitly distinguishes itself by noting it does NOT probe each tool individually, which helps differentiate it from sibling data-lookup tools.

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 explicit WHEN TO USE guidance ('agent startup, before high-stakes task sequences, or after unexpected errors') and WHEN NOT TO USE ('Do not call more than once per minute'). It provides an example user query. It does not explicitly compare itself to sibling tools, but the scope is clear enough for selection.

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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TDQS

A4.1/5.0
Disambiguation4/5

The three company-verification tools (verify_company_record, screen_sanctions, lookup_us_contracts) are clearly distinct by data source and purpose. get_status and get_outcome are the main potential confusion, but their lifecycle boundary (pending vs completed) is explicitly described.

Naming Consistency2/5

Naming mixes multiple verb styles: get_outcome/get_status, lookup_us_contracts, preview_cost, screen_sanctions, self_test, verify_company_record. There is no consistent verb_noun or resource-oriented pattern across the set.

Tool Count4/5

Seven tools is a reasonable count for a company-verification/due-diligence server. However, three of them (get_status, get_outcome, preview_cost) are generic infrastructure that feel bolted on rather than part of the core verification workflow.

Completeness3/5

The core verification surface covers company registry existence, sanctions screening, and US federal contracts, which addresses common due-diligence queries. The set is incomplete as a whole because get_status/get_outcome depend on async operations that no included tool can initiate, and preview_cost references operations unrelated to company verification.