Skip to main content
Glama

mcp-knowledge

by io.github.getgapup

reputation_engine

Read-only

Moteur de réputation — Gapup agent-payable C-suite expertise (CMO). Returns a structured, audited deliverable. Reference case: PayShield SaaS — Monitoring réputation Q2 2026. Inputs are validated server-side — send the documented case fields.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
asyncNoIf true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.
brandYes
channelsYes
industryYes
keywordsYes
historicalCrisesNo

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed13 schema fields changed
    • addedInput schema / properties / brand / maxLength
      Added value: +200
    • addedInput schema / properties / brand / minLength
      Added value: +2
    • addedInput schema / properties / channels / items
      Added value: +{
+  "enum": [
+    "twitter",
+    "linkedin",
+    "g2",
+    "trustpilot",
+    "news",
+    "reddit",
+    "glassdoor",
+    "youtube",
+    "instagram",
+    "other"
+  ],
+  "type": "string"
+}
    • addedInput schema / properties / channels / maxItems
      Added value: +10
    • addedInput schema / properties / channels / minItems
      Added value: +1
    • addedInput schema / properties / historicalCrises / items
      Added value: +{
+  "maxLength": 300,
+  "minLength": 5,
+  "type": "string"
+}
    • addedInput schema / properties / historicalCrises / maxItems
      Added value: +5
    • addedInput schema / properties / industry / maxLength
      Added value: +150
    • addedInput schema / properties / industry / minLength
      Added value: +2
    • addedInput schema / properties / keywords / items
      Added value: +{
+  "maxLength": 100,
+  "minLength": 2,
+  "type": "string"
+}
    • addedInput schema / properties / keywords / maxItems
      Added value: +20
    • addedInput schema / properties / keywords / minItems
      Added value: +1
    • addedInput schema / required
      Added value: +[
+  "brand",
+  "keywords",
+  "channels",
+  "industry"
+]
  2. First observed

TDQS

C2.6/5.0
Behavior3/5

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

Annotations already provide readOnlyHint and openWorldHint. The description adds that the deliverable is 'audited' and that inputs are validated server-side, which provides some extra context but does not go into rate limits, auth needs, or other behavioral traits. No contradiction with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is short (3 sentences) and front-loaded with the name and purpose. However, it includes cryptic jargon ('Gapup agent-payable C-suite expertise (CMO)') and a specific reference case that may not be helpful for general understanding.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With 6 parameters, low schema coverage, and no output schema, the description is insufficient. It does not explain what the deliverable contains, how to use the async parameter, or the purpose of each input field. Annotations provide some context but not enough for a complete understanding.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is only 17%, and only the 'async' parameter has a description in the schema. The tool description does not explain the meaning or constraints of the other 5 parameters (brand, keywords, channels, industry, historicalCrises). 'Send the documented case fields' is vague and does not add semantic value.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states the tool is a 'Moteur de réputation' (reputation engine) that returns a 'structured, audited deliverable,' giving a clear verb-resource pair. However, it lacks precision about what the tool actually does with the inputs (e.g., monitors, calculates, analyzes reputation) and does not distinguish it from sibling tools like sentiment_news_pulse or brand_equity_voice_share_calculator.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no guidance on when to use this tool versus alternatives, no explicit context for usage, and no mention of when not to use it. The description only states inputs are validated server-side, which is not usage guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All mcp-knowledge toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

C2.8/5.0
Disambiguation2/5

Many tools have overlapping purposes, especially in competitive intelligence, ESG, and risk assessment. For example, there are multiple tools for competitor analysis (competitive_deep_dive, competitor_intel, competitor_moves, etc.) with unclear boundaries. Agents would struggle to select the correct tool without deep understanding of subtle differences.

Naming Consistency2/5

Tool names are a mix of English and French, and follow no consistent pattern. Some use snake_case (e.g., abm_architect, action_plan_esg), while others are verb-focused (e.g., content_catalog, fx_rate). The lack of a uniform naming convention makes it hard for agents to predict tool names.

Tool Count1/5

With 271 tools, the server is excessively large. Even for a broad knowledge domain, this number of tools makes discovery and selection inefficient. Typical coherent servers have 3-15 tools; this has an order of magnitude more, indicating poor scoping.

Completeness3/5

The tool set covers many domains (compliance, finance, marketing, HR, etc.), but the coverage is uneven due to redundancy. Key areas have multiple overlapping tools, while some sub-domains may still have gaps. Overall, the surface is broad but not well-curated.

Resources