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Data Michigan

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

A4.5/5.0
Behavior5/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false, idempotent=true, indicating safe, idempotent read. The description adds valuable behavior: returns '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.' This goes beyond annotations by explaining the output format and usability, with no contradictions.

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 (3 sentences) and front-loaded: first sentence states purpose, second lists domains, third explains return format and usage guidance. Every sentence adds value with no redundancy or filler.

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?

Given the tool's complexity (search for tools with return of schemas), the description fully covers usage context ('call this FIRST'), return structure (names, descriptions, schemas, examples), and parameter aliases. No output schema exists, but the description adequately explains what is returned.

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 coverage is 100% (6 parameters fully described). The description mentions the main 'query' parameter and its aliases, but does not add substantial meaning beyond the schema. Baseline 3 is appropriate since the schema already provides adequate documentation.

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 many example domains, making the scope explicit. The verb 'discover' and resource 'tools' are specific, and sibling tools are distinct (e.g., specific tools like query, datasets), so differentiation is clear.

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 advises 'Call this FIRST when you have many tools available and want to see the option set (not just one answer).' This provides clear context for when to use it, though it does not explicitly state when not to use it (e.g., for a single direct query). Still, the guidance is strong and practical.

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

A3.8/5.0
Disambiguation2/5

There are multiple severe overlap clusters. ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route to the same 5,578 tools, and ask_pipeworx_beta explicitly states it 'currently matches ask_pipeworx exactly' — a direct ambiguity. The six polymarket_* tools plus bet_research form another dense, hard-to-distinguish cluster, and entity_profile/recent_changes/compare_entities/resolve_entity all have overlapping entity-investigation purposes. The long descriptions help but an agent would frequently misselect.

Naming Consistency3/5

The dominant families are internally consistent (polymarket_* prefix, ask_pipeworx_* suffix family, and the verb-based remember/recall/forget), which aids navigation. However, the overall set mixes several conventions: single-word nouns (query, datasets, metadata, recall), verb_noun compounds (validate_claim, generate_llms_txt), and domain_noun names (entity_profile, polymarket_edges). Readable, but there is no unified pattern across the server.

Tool Count2/5

34 tools is clearly over the 25-threshold for heaviness, and several earn little distinct value: ask_pipeworx_beta is a live duplicate of ask_pipeworx, the five-algorithm Polymarket family could be consolidated, and meta/utility tools (suggest_questions, discover_tools, pipeworx_trending, generate_llms_txt, scan_dependency) feel bolted on rather than essential. The breadth of the data domain justifies some size, but the redundancy and tangents push it into bloat.

Completeness3/5

Within its core sub-domains the surface is fairly complete: company research has resolve→profile/compare→recent_changes→validate_claim as a full lifecycle, subscriptions have subscribe/unsubscribe/list/recent_alerts, and memory has remember/recall/forget. The Polymarket workflow is especially thorough (detect→verify→fill-risk→track-decay). However, the server's stated identity ('Data Michigan') is barely served — the Michigan Open Data surface is only search/schema/query with no update or write path — and the scatter of unrelated tools (npm dependency scan, llms.txt generation) makes the overall purpose incoherent, so gaps are hard to evaluate.

Resources