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gapup-mcp

by io.github.getgapup

qbr_auto

Read-only

QBR automatique CSM — Gapup agent-payable C-suite expertise (CRO). Returns a structured, audited deliverable. Reference case: Gapup Hub × Alan — QBR Q1 2026 · Health score 82/100 · Upsell €18k détecté · Renewal low risk. Inputs are validated server-side — send the documented case fields.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
winsYes
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.
periodYes
companyYes
metricsYes
customerYes
challengesYes
nextQuarterGoalsYes

Schema Changelog

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

  1. First observed

TDQS

D1.6/5.0
Behavior2/5

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

Annotations already declare readOnlyHint=true and openWorldHint=true. The description adds 'validated server-side' and 'audited deliverable,' but these are vague and fail to provide meaningful behavioral context about processing, output details, or limitations.

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

Conciseness2/5

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

The description is cluttered with branding and an irrelevant reference case ('Gapup Hub × Alan — QBR Q1 2026 ...'). It is not concise or front-loaded, mixing French and English while omitting essential functional details.

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

Completeness1/5

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

For a tool with 8 parameters, 7 required, nested objects, and no output schema, the description is severely incomplete. It does not explain the deliverable's content, expected metric formats, or how the tool relates to similar QBR-related sibling tools.

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

Parameters1/5

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

Schema description coverage is only 13% (only 'async' has a description). The description says 'send the documented case fields' but does not enumerate or explain any of the 8 parameters or nested object fields, providing no compensation for the sparse schema.

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

Purpose2/5

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

The description's core phrase 'QBR automatique CSM' largely restates the tool name/title without specifying the action. 'Returns a structured, audited deliverable' is generic and does not clarify what the tool actually does or how it differs from siblings like renewal_optimizer or upsell_hunter.

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

Usage Guidelines1/5

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. The only directive is 'Inputs are validated server-side — send the documented case fields,' which concerns input submission rather than usage context or exclusions.

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

C2.4/5.0
Disambiguation1/5

Over 50 tools share the identical template 'Gapup agent-payable C-suite expertise' with similar French descriptions and reference cases, making their boundaries indistinguishable. Clusters like competitor_intel, competitive_deep_dive, competitor_moves, competitor_profiles, competitor_pricing_radar, competitor_pricing_scrape, and competitor_recommendations heavily overlap in purpose.

Naming Consistency1/5

Names are chaotic: mix of French and English, snake_case and camelCase, verb_noun, noun, and adjective forms with no uniform pattern. Examples like 'bp_narratif', 'content_enrichment', 'ai_governance_full_report_async', and 'job_result' show no coherent naming convention.

Tool Count1/5

271 tools is far beyond any reasonable MCP server scope, creating an overwhelming selection burden for agents. This count vastly exceeds the 25+ threshold for 'too many' and makes navigation impractical.

Completeness2/5

While the server covers many business domains, it lacks lifecycle operations (e.g., no update/delete tools for the deliverables it generates) and the input specifications are vague ('documented case fields' without documentation), creating functional dead ends. The sheer breadth does not compensate for these gaps.

Resources