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Glama

Wikidata

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

A4.8/5.0
Behavior5/5

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

Annotations already indicate readOnly, idempotent, non-destructive. The description adds that it returns top-N tools with names, descriptions, full schemas with examples, and that results are callable directly—valuable context 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.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

One well-structured paragraph with front-loaded purpose. Contains necessary detail but could be tighter; still very effective for agent consumption.

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?

No output schema, but description fully explains what is returned (names, descriptions, schemas, examples). Covers default/max limit. All relevant behavioral aspects are addressed given the tool's simplicity.

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?

Schema coverage is 100% so baseline is 3. Description adds context: clarifies that 'query' accepts natural language, lists aliases (task, q, search, description), and explains the limit parameter. This adds moderate value beyond the schema.

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 specific examples (SEC filings, FDA drugs, etc.) and explains that it returns relevant tools ready to call, distinguishing it from sibling tools that perform different functions.

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 tells the agent when to use it and implies alternatives (e.g., if you already know the tool, skip).

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

A3.5/5.0
Disambiguation2/5

The tool set is extremely heterogeneous and overlapping. Multiple tools serve nearly identical purposes (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, discover_tools, suggest_questions), and entity-related tools (get_entity, get_wikidata_facts, entity_profile, compare_entities, resolve_entity) have unclear boundaries. The server is named Wikidata but only 3 of 34 tools actually relate to Wikidata, causing confusion about which tool is appropriate.

Naming Consistency2/5

Tool names mix multiple conventions: verb_noun (get_entity, search_entities, list_subscriptions), noun phrases (polymarket_edges, entity_profile, pipeworx_feedback), and inconsistent suffixes (_beta, _grounded). The verbs used are vague and not part of a coherent pattern (get, search, ask, discover, resolve, scan, validate, generate, remember, recall). The naming is readable but lacks a predictable system.

Tool Count2/5

34 tools is high for a server with a focused name like Wikidata, and nearly all tools belong to unrelated domains (Pipeworx data routing, Polymarket betting, memory management, subscriptions, AI visibility). The count appears bloated and scattered rather than well-scoped; most tools could be split into separate servers.

Completeness2/5

For a Wikidata server, the coverage is minimal: only search, get by ID, and human-readable facts are present, with no SPARQL query support or property-level access. The majority of tools address Pipeworx's broad data catalog, leaving the advertised Wikidata purpose severely under-served. The surface is complete for a different domain but incomplete for the stated one.

Resources