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Glama

colorapi

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

A4.7/5.0
Behavior5/5

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

Annotations already indicate readOnly, idempotent, and non-destructive behavior. The description goes beyond by detailing the return format: '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 adds valuable context about output and eliminates ambiguity.

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 front-loaded with the core purpose, followed by usage guidance, then return details, and closes with tactical advice. Every sentence adds distinct value, and no filler is present. Length is justified for a discovery tool that needs to convey its expansive scope.

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 no output schema, the description compensates by explaining exactly what will be returned (names, descriptions, full schemas, curated examples) and the benefit of being call-ready. It also covers the broad range of supported data domains, making it complete for a tool of this complexity.

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%, and the schema already explains the 'query' parameter with aliases and examples. The description says 'by describing the data or task' which aligns with the schema but does not add new parameter-level details. Baseline 3 is appropriate since the schema carries the load.

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 verb and resource: 'Find tools by describing the data or task.' It distinguishes itself from siblings by positioning as a meta-tool for discovery ('Call this FIRST when you have many tools available'), which is unique among the listed 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?

Explicitly states when to use: 'Use when you need to browse, search, look up, or discover what tools exist' and provides a when-not condition: 'not just one answer.' It also gives strategic advice to call this tool first when many tools are available, which serves as clear usage guidance.

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

A4/5.0
Disambiguation3/5

Some tools have distinct purposes (e.g., color tools, memory tools), but many data query tools (ask_pipeworx, ask_pipeworx_grounded, deep_research, entity_profile) overlap significantly, making it hard for an agent to choose the right one without deep knowledge of their nuances.

Naming Consistency4/5

Most tool names use snake_case with a verb_noun pattern (e.g., convert_color, identify_color, resolve_entity), but there is inconsistency in prefixes: some use 'ask_pipeworx', others 'pipeworx_', 'polymarket_', or 'scan_'. Overall, still readable and predictable.

Tool Count3/5

33 tools is on the high side for a single server, especially one named 'colorapi' which misleadingly suggests a focus on color only. The tool count is borderline appropriate for the actual broad data integration scope, but the mismatch with the server name is problematic.

Completeness4/5

The tool set covers a wide range of data sources and operations (SEC, FDA, patents, news, Polymarket, memory, subscriptions), with few obvious gaps. Minor lacks: no direct tool for updating stored memories or handling non-US companies, but overall comprehensive.

Resources