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Parse

by gl.parse.mcp
OverviewDiscussionsAdmin

Search Parse AI visibility data

parse_search
Read-onlyIdempotent

Discover brands, competitors, buyer topics and markets in the free public AI visibility index. For one named brand or website, use parse_check_brand directly. Search returns bounded candidates, domain/family context, source links and exact follow-up arguments; it does not claim complete coverage. Narrow the query if needed.

Input Schema

NameRequiredDescriptionDefault
limitNo
queryYesA brand name, a website, a market, a topic or a question.
typesNoWhich kinds to return; omit for all three. Questions select the topics they belong to.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • changedInput schema / properties / types / description
      Previous value: -"Which kinds to return; omit for all three. `niches` are markets. `prompts`, the old name for questions, returns topics."New value: +"Which kinds to return; omit for all three. Questions select the topics they belong to."
    • changedInput schema / properties / types / items / enum
      Previous value: -[
-  "brands",
-  "topics",
-  "prompts",
-  "niches"
-]New value: +[
+  "brands",
+  "topics",
+  "questions",
+  "markets"
+]
  2. Changed1 schema field changed
    • changedInput schema / properties / types / description
      Previous value: -"Which kinds to return; omit for all four. Two values keep their code names: `prompts` are questions and `niches` are markets."New value: +"Which kinds to return; omit for all three. `niches` are markets. `prompts`, the old name for questions, returns topics."
  3. Changed3 schema fields changed
    • changedInput schema / properties / query / description
      Previous value: -"A brand name, a website, a category or a question."New value: +"A brand name, a website, a market, a topic or a question."
    • addedInput schema / properties / types / description
      Added value: +"Which kinds to return; omit for all four. Two values keep their code names: `prompts` are questions and `niches` are markets."
    • changedInput schema / properties / types / items / enum
      Previous value: -[
-  "brands",
-  "prompts",
-  "niches"
-]New value: +[
+  "brands",
+  "topics",
+  "prompts",
+  "niches"
+]
  4. Changed1 schema field changed
    • addedInput schema / properties / query / description
      Added value: +"A brand name, a website, a category or a question."
  5. Changed1 schema field changed
    • changedInput schema / properties / types / items / enum
      Previous value: -[
-  "brands",
-  "prompts",
-  "niches",
-  "sources"
-]New value: +[
+  "brands",
+  "prompts",
+  "niches"
+]
  6. First observed

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already declare the safety profile (readOnly, non-destructive, idempotent, closed-world), so the bar is lower. The description adds real behavioral context beyond that: results are 'bounded candidates' with 'domain/family context, source links and exact follow-up arguments,' and it candidly states it 'does not claim complete coverage' — an important expectation-setting limit.

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?

Four sentences, purpose front-loaded, with the sibling routing and result-characteristics sentences each earning their place. No filler, though the final 'Narrow the query if needed' is a slightly thin trailing clause.

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?

There is no output schema, so the description carries the burden of describing returns — it does so (bounded candidates, domain/family context, source links, follow-up arguments) and sets coverage expectations. Only the undocumented 'limit' parameter and 'free public index' access implications remain thin.

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 coverage is 67%: 'query' and 'types' are documented, but 'limit' has no description anywhere. The description's list of content kinds loosely maps to the types enum values, but adds no syntax or format detail beyond the schema; this is the baseline case where the schema does most of the work.

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?

States a specific verb ('Discover') and resource ('brands, competitors, buyer topics and markets in the free public AI visibility index'), and explicitly contrasts itself with the sibling parse_check_brand. An agent can tell it apart from parse_check_brand, parse_get_brand and the generic search without opening any schema.

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?

Gives an explicit routing rule: 'For one named brand or website, use parse_check_brand directly,' which tells the agent when this tool is not the right pick. Adds a fallback hint ('Narrow the query if needed') but does not address other plausible alternatives such as fetch or search, so it falls short of full alternative coverage.

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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