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Glama

Colorado Information Marketplace

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

A4.4/5.0
Behavior4/5

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

Annotations already indicate read-only and idempotent behavior. The description adds valuable context: it returns top-N tools with full schemas and examples, ready to call directly, and requires no second lookup. This goes beyond the 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 3 sentences, each adding value. It front-loads the purpose, provides usage context, and details the return format without redundancy.

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 discovery tool with no output schema, the description adequately explains the return value (names, descriptions, schemas with examples) and usage pattern. It could be more explicit about result structure, but the stated information is sufficient for an agent to use the tool.

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% (all 6 parameters have descriptions), so the baseline is 3. The description does not add significant parameter-level detail beyond what the schema provides, though it contextualizes the 'query' parameter with examples.

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 states the tool's purpose: 'Find tools by describing the data or task.' It lists numerous domains (SEC filings, FDA drugs, etc.) and distinguishes it from siblings by framing it as a discovery tool to be called first, contrasting with tools that operate on specific data.

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?

Explicit guidance: 'Call this FIRST when you have many tools available and want to see the option set (not just one answer).' This clearly indicates when to use and implies that for a direct answer, other tools may be more appropriate.

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

All Colorado Information Marketplace toolsInspect
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TDQS

A4/5.0
Disambiguation3/5

The tool set has several overlapping clusters: ask_pipeworx / ask_pipeworx_beta / ask_pipeworx_grounded, entity_profile / recent_changes / compare_entities, and a half-dozen prediction-market tools. The descriptions are unusually detailed and mostly steer an agent correctly, but ask_pipeworx_beta is currently identical to ask_pipeworx and the prediction-market tools still require careful reading to pick the right one.

Naming Consistency3/5

All names are lowercase snake_case, but the conventions vary: many are verb_noun (resolve_entity, compare_entities, validate_claim), some are bare nouns (datasets, metadata, query), some are bare imperatives (remember, forget, subscribe), and there are separate prefix families like polymarket_* and pipeworx_*. It is readable and consistent in style, but not a single predictable pattern.

Tool Count2/5

34 tools exceeds the 25+ threshold for 'too many,' and the set is not tightly scoped: only datasets, metadata, and query directly relate to the stated Colorado Information Marketplace purpose. The bulk are Pipeworx data-research, prediction-market, memory, and subscription utilities, making the server feel like a broad platform bolted onto a state-data catalog.

Completeness4/5

For the core read-only lifecycle of the Colorado data catalog, search (datasets), schema inspection (metadata), and data retrieval (query) are covered. Minor gaps exist elsewhere: there is no explicit tool for fetching a pipeworx:// citation record directly, and some utilities like generate_llms_txt or scan_dependency are unrelated to the server's stated purpose, but most cited workflows can still complete.

Resources