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Glama

Gdelt

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. Changed5 schema fields changed
    • addedInput schema / properties / description
      Added value: +{
+  "description": "Alias for query.",
+  "type": "string"
+}
    • addedInput schema / properties / q
      Added value: +{
+  "description": "Alias for query.",
+  "type": "string"
+}
    • changedInput schema / properties / query / description
      Previous value: -"Natural language description of what you want to do (e.g., \"analyze housing market trends\", \"look up FDA drug approvals\", \"find trade data between countries\")"New value: +"Natural 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."
    • addedInput schema / properties / search
      Added value: +{
+  "description": "Alias for query.",
+  "type": "string"
+}
    • addedInput schema / properties / task
      Added value: +{
+  "description": "Alias for query.",
+  "type": "string"
+}
  2. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "query": "look up FDA drug approvals"
+  },
+  {
+    "query": "analyze housing market trends"
+  }
+]
  3. 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 readonly and idempotent hints. The description adds valuable behavioral details: returns top-N tools with full schemas and examples, ready to call directly, no second lookup. 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.

Conciseness4/5

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

The description is moderately concise, front-loaded with purpose, and contains no filler. Could be slightly shorter but 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?

Without an output schema, the description adequately explains the return format (top-N tools with names, descriptions, schemas). It covers usage context ('Call this FIRST') and parameter aliases.

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. The description adds beyond schema by explaining the query parameter accepts natural language and lists aliases, enhancing usability.

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 explicitly states 'Find tools by describing the data or task' and lists many domains, clearly distinguishing it from sibling tools which are specific to certain tasks (e.g., bet_research, search_articles).

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?

The description explicitly advises 'Call this FIRST when you have many tools available and want to see the option set', providing clear context for when to use this tool. It does not explicitly state when not to use it, but the guidance is effective.

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

Multiple tools have unclear boundaries: ask_pipeworx, ask_pipeworx_beta (explicitly identical today), and ask_pipeworx_grounded are near-duplicates, while deep_research, entity_profile, compare_entities, and bet_research all route into the same underlying Pipeworx catalog. ai_visibility_check and scan_competitor_ai_presence also overlap heavily. Descriptions are detailed, but an agent could easily misselect among the research and market-analysis clusters.

Naming Consistency3/5

All names use lowercase snake_case, but the pattern is mixed: verb-first names (search_articles, resolve_entity, validate_claim), noun-phrase domain tools (entity_profile, polymarket_fill_risk), brand-prefixed names (pipeworx_trending, ask_pipeworx), and bare verbs (recall, remember, forget). It is readable and consistent in style, but there is no predictable verb_noun convention and tool names do not reliably indicate their domain.

Tool Count2/5

With 35 tools, this set is well beyond the 3-15 well-scoped range and even the 16-25 heavy range. Several clusters could be consolidated (three ask_pipeworx variants, five polymarket analysis tools, three memory tools), and unrelated utilities like generate_llms_txt and scan_dependency add to the sprawl.

Completeness4/5

For a data-research server, coverage is broad: universal lookup, grounded answers, deep research, entity profiles, comparisons, news search/sentiment/timelines, prediction-market analysis, and memory/subscription lifecycle tools are all present. The main gaps are direct raw-document fetching (e.g., full article text or a specific SEC filing body), but the universal ask_pipeworx router and pipeworx:// resource URIs let agents work around those.

Resources