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Glama

Google_analytics

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

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so safety profile is covered. The description adds value by explaining the return output (top-N tools with full schemas, no second lookup needed) and the 'ready to call directly' property. It doesn't discuss edge cases like no matches, but that's acceptable given the 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?

The description is well-structured with a clear opening, a domain list, return behavior, and usage directive. It is slightly long due to the domain enumeration, but each sentence serves a purpose and the structure is logical.

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?

For a discovery tool with one required parameter and high schema coverage, the description covers purpose, usage, return format, and positioning relative to siblings. No output schema exists, but the description sufficiently explains what the agent will receive. This is complete for the tool's 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?

The schema description coverage is 100%, with every parameter documented including aliases. The description only paraphrases the query concept and references 'top-N' (limit). It adds no meaningful semantics beyond what the schema already provides, so the baseline score of 3 is appropriate.

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 identifies the tool as a meta-search over other tools ('Find tools by describing the data or task'), listing specific domains and distinguishing itself from siblings by being the 'discover' tool. It explicitly tells the agent to call it first when many tools are available, which sets it apart from domain-specific siblings.

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?

The description specifies when to use it ('browse, search, look up, or discover what tools exist'), provides concrete examples, and gives the key directive 'Call this FIRST when you have many tools available and want to see the option set'. This is strong guidance for selection.

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

Several tools overlap heavily: ask_pipeworx and ask_pipeworx_beta are explicitly identical, ask_pipeworx_grounded and deep_research both route queries to the same 5,529 tools, and ai_visibility_check vs scan_competitor_ai_presence are near-duplicates. Only the small ga_* subset is clearly distinct.

Naming Consistency2/5

Conventions are mixed: GA tools use a ga_ prefix, but the majority use arbitrary names like ask_pipeworx, deep_research, remember, scan_dependency, and polymarket_arbitrage. No consistent verb_noun pattern across the set.

Tool Count2/5

35 tools is heavy, and the vast majority (31) are unrelated Pipeworx utilities rather than Google Analytics functionality. Only 4 tools actually serve the stated GA purpose, making the count inappropriate for the server name.

Completeness2/5

For Google Analytics, the surface is minimal: list properties, metadata, realtime, and run report—no property management, user management, or data mutation. As a Pipeworx toolkit it's broad but lacks full lifecycle coverage for any single domain.

Resources