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Glama

Missouri License Offices

by io.github.pipeworx-io

Discover Tools

discover_tools
Read-onlyIdempotent

Find tools by describing the data or task. Use when you need to browse, search, look up, or discover what tools exist for: SEC filings, financials, revenue, profit, FDA drugs, adverse events, FRED economic data, Census demographics, BLS jobs/unemployment/inflation, ATTOM real estate, ClinicalTrials, USPTO patents, weather, news, crypto, stocks. Returns the top-N most relevant tools with names, descriptions, and full input schemas (with curated examples) — each result is ready to call directly, no second schema lookup needed. Call this FIRST when you have many tools available and want to see the option set (not just one answer).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qNoAlias for query.
taskNoAlias for query.
limitNoMaximum number of tools to return (default 20, max 50)
queryYesNatural language description of what you want to do (e.g., "analyze housing market trends", "look up FDA drug approvals", "find trade data between countries"). Accepts task, q, description, search as aliases.
searchNoAlias for query.
descriptionNoAlias for query.

Schema Changelog

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

  1. First observed

TDQS

A4.2/5.0
Behavior4/5

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

Annotations declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the description does not contradict them. The description adds value by disclosing that results include full schemas with curated examples and are ready to call, which goes beyond annotations.

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 concise with no fluff. Front-loaded with the primary action and key usage scenario, then lists specific domains and what results contain. Every sentence adds value.

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?

Although there is no output schema, the description adequately explains what the tool returns (top-N tools with names, descriptions, full schemas). It mentions curated examples and that results are ready to call. Given the tool's complexity and lack of output schema, this is sufficient.

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 100%, with all 6 parameters documented. The description provides no additional meaning beyond repeating that query is a natural language description and noting aliases, which are already in the schema. Baseline of 3 is appropriate since schema does the heavy lifting.

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 uses a specific verb ('Find tools by describing') and clearly identifies the resource (tools). It distinguishes itself from siblings by focusing on discovery of available tools, while siblings like 'search_within' or 'deep_research' handle data within tools. The extensive domain list further clarifies scope.

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?

Explicitly states 'Call this FIRST when you have many tools' which provides clear when-to-use guidance. It implies using this before other tools to see options. However, it does not explicitly mention when not to use or compare with alternatives, though context is sufficient.

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

All Missouri License Offices toolsInspect
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TDQS

A3.9/5.0
Disambiguation1/5

Multiple tools overlap heavily: ask_pipeworx, ask_pipeworx_beta (explicitly identical today), ask_pipeworx_grounded, deep_research, validate_claim, and discover_tools all route natural-language data questions, while entity_profile, recent_changes, compare_entities, and resolve_entity overlap around company data. An agent cannot reliably distinguish which retrieval entry point to choose.

Naming Consistency4/5

Tool names are almost uniformly lowercase snake_case and mostly follow a recognizable verb_noun or domain-prefixed pattern (ask_pipeworx*, polymarket_*, pipeworx_*, scan_*, recent_*, subscribe/unsubscribe). A few names like deep_research, entity_profile, and bet_research break the verb-first style, but there is no chaotic convention mixing.

Tool Count2/5

32 tools is too many for the apparent scope, and more importantly only one tool (mo_dmv_license_offices) matches the server name 'Missouri License Offices.' The other 31 tools form a general data-research, prediction-market, memory, and subscription platform that has little to do with the stated purpose.

Completeness4/5

Judged as the broad Pipeworx-style research platform the descriptions reveal, the surface is quite complete: simple and grounded querying, deep multi-source research, claim verification, entity resolution, profiles, comparisons, change feeds, discovery, subscriptions, alerts, and memory. For the literal Missouri license-office purpose, however, only the single lookup tool is present, which drags down completeness despite that tool being reasonably thorough.

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