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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

A4.9/5.0
Behavior5/5

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

Annotations already mark it read-only and idempotent, but the description adds valuable behavior beyond that: exactly 6 internal checks, reports how many passed, does not probe each tool individually, requires no key, has ~200ms latency, and carries a rate limit. These operational details help the agent understand side effects and constraints.

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 well-structured and front-loaded: core behavior first, then an example, then usage guidance, cost, and latency. Every section contributes practical information without redundant filler or excessive length.

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 health probe with rich annotations, the description covers everything needed to invoke it correctly: purpose, result shape, timing, rate limit, authentication requirement, and performance expectation. The absence of an output schema is acceptable because the description states what the tool reports.

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 takes zero parameters, and the schema description coverage is 100%, so there is no parameter ambiguity. The example call self_test({}) reinforces that no arguments are needed, which 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?

States a specific verb and resource: 'runs 6 internal checks and reports how many passed'. It also explicitly clarifies the tool's scope by saying it 'does NOT probe each tool individually', which distinguishes it from sibling tools that verify or probe individual business entities.

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

Usage Guidelines5/5

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

Provides explicit WHEN TO USE and WHEN NOT TO USE sections: at startup, before high-stakes task sequences, after unexpected errors, and no more than once per minute in production. The example user query also shows a concrete matching scenario, leaving little ambiguity for the agent.

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

A4.2/5.0
Disambiguation4/5

Most tools have clearly differentiated targets (e.g., check_booking_link vs import_booking_url, get_status vs get_outcome), and the descriptions are unusually thorough. However, send_message and send_transactional_confirmation overlap because send_message already includes a 'transactional' message type, and verify_business vs verify_company_record are easy to confuse despite different scopes.

Naming Consistency5/5

All 23 tools use a consistent verb_noun snake_case pattern (check_*, get_*, send_*, verify_*), with no camelCase or stylistic drift. The verb uniformly precedes the object, making the surface predictable and easy to navigate.

Tool Count3/5

23 tools is on the heavy side for a single MCP server, spanning SMB communications, booking, compliance screening, trade lookup, and platform utilities. Each tool has a purpose, but the count pushes the set into the 16-25 borderline range and suggests scope creep.

Completeness3/5

Core workflows for booking, messaging, and compliance pre-flight are well covered, including async polling and cost preview. However, there are lifecycle gaps: capture_lead has no way to list/update/retrieve leads, and business records support import/verify but no update/delete. The trade/company-verification tools also feel disconnected from the main SMB flow.