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Glama

Performance Review

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

A4.7/5.0
Behavior5/5

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

Annotations declare readOnlyHint and idempotentHint true, destructiveHint false. The description adds behavioral details beyond annotations: returns top-N relevant tools with full schemas and curated examples, ready to call directly. 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 fairly concise but front-loaded with the core purpose. It includes a list of example domains which is helpful but could be slightly trimmed. Still well-organized and informative.

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?

Despite no output schema, the description explains what is returned (names, descriptions, schemas with examples) and notes the limit parameter's range. For a discovery tool with high annotation coverage, this is sufficiently complete.

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%, but the description adds context by explaining that query accepts natural language descriptions like 'analyze housing market trends' and lists aliases. This enriches understanding. Slight deduction for not specifying the default limit value in the description.

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 specific domains and tasks (SEC filings, FDA drugs, etc.), distinguishing this discovery tool from its siblings which are specialized 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: 'Call this FIRST when you have many tools available and want to see the option set (not just one answer).' This tells the agent precisely when to use this tool vs. others.

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

A3.9/5.0
Disambiguation3/5

Several tools occupy adjacent territory: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all answer questions with only subtle differences in grounding and breadth, and discover_tools overlaps with suggest_questions. The prediction-market tools (polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread, bet_research) each have distinct mechanics but a less careful agent could easily misfire between them.

Naming Consistency3/5

Most tools follow a verb_noun pattern (ask_pipeworx, compare_entities, generate_llms_txt, list_subscriptions), but there are notable exceptions: performance_review_generate reverses the order, entity_profile is noun_noun, and recall/remember/forget are bare verbs. The pipeworx_ and polymarket_ prefixes help group families, but the mixed styles prevent a higher score.

Tool Count2/5

At 32 tools this is a heavy surface for one MCP server, exceeding the 25+ threshold where agents struggle to discover and choose among options. The breadth is understandable given the many subdomains (data lookups, prediction markets, memory, subscriptions, feedback), but the count still feels like tool sprawl rather than a tightly scoped set.

Completeness4/5

As a data/research/prediction-market platform the surface is remarkably complete: query, grounded verification, deep research, entity resolution, comparisons, claim validation, visibility audits, subscriptions, memory, and feedback loops are all covered. Minor gaps exist—there is no direct tool for bulk exports or for editing subscriptions, and some meta-tools duplicate discovery—but agents can accomplish the core workflows without dead ends.

Resources