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Glama

advice

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. Added

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds valuable behavioral context: returns top-N relevant tools with full input schemas and curated examples, ready to call directly without a second lookup. It goes beyond annotations by describing the return format and direct callability.

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 dense but well-structured: first sentence defines purpose, second gives usage context, third explains return behavior, fourth gives strategic guidance. Every sentence earns its place, with no redundancy. Front-loaded with the action verb and resource.

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?

Despite having no output schema, the description fully explains what the tool returns (names, descriptions, schemas, examples) and why it is useful. Combined with the rich annotation set, the description is complete for a meta-tool with 6 params and many aliases.

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 every parameter (including aliases) documented. The description itself doesn't add further parameter semantics; it mentions 'describing the data or task' but relies on the schema's examples. Baseline 3 is appropriate since structured data 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 clearly states the tool's purpose: 'Find tools by describing the data or task.' It lists many domains (SEC filings, FDA drugs, FRED data, etc.) and explicitly distinguishes itself as a meta-tool: 'Call this FIRST when you have many tools available.' This is a specific verb+resource with clear differentiation from sibling tools.

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 usage guidance is provided: 'Use when you need to browse, search, look up, or discover what tools exist' and 'Call this FIRST when you have many tools available and want to see the option set (not just one answer).' It communicates when to use the tool and implies when not to (when you already know the specific tool).

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

A3.7/5.0
Disambiguation2/5

The ask_pipeworx family is a major confusion source: ask_pipeworx_beta explicitly states it 'currently matches ask_pipeworx exactly', and ask_pipeworx_grounded/deep_research heavily overlap with the base router. polymarket_edges vs polymarket_arbitrage and discover_tools vs suggest_questions also have fuzzy boundaries, though long descriptions partially mitigate the overlap.

Naming Consistency4/5

All tool names are lowercase snake_case, and most follow a verb_noun pattern (validate_claim, resolve_entity, compare_entities, generate_llms_txt). A few bare verbs (remember, recall, forget) and noun-style names (entity_profile, polymarket_arbitrage, pipeworx_trending) deviate slightly, but the overall style is predictable and readable.

Tool Count2/5

34 tools is heavy and spans several unrelated domains: Pipeworx data research, prediction markets, subscriptions, memory, AI visibility, advice slips, npm dependency checks, and llms.txt generation. The count is inflated by near-duplicate research routers and disconnected outliers like generate_llms_txt and scan_dependency, making the set feel like a kitchen sink rather than a focused server.

Completeness3/5

The Pipeworx research and prediction-market surfaces are quite complete (ask, grounded, deep research, entity profile, compare, resolve, validate, subscriptions with full lifecycle, memory with save/recall/delete). However, the server is named 'advice' yet the advice domain only has three thin tools (get/search/random) with no other operations, and the mixed domains leave obvious dead ends for any single stated purpose.

Resources