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sales_pipeline_forecast

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Prévision de pipeline commercial — Gapup agent-payable C-suite expertise (CRO). Returns a structured, audited deliverable. Reference case: Doctolib Enterprise — pipeline Q2 2026 · 50 deals enterprise/mid-market · forecast confidence par deal + commit/best-case/worst-case. 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.
focusNo
companyYes
pipelineYes
historicalConversionByStageNo

Schema Changelog

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

  1. First observed

TDQS

C2.8/5.0
Behavior3/5

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

The annotations already declare readOnlyHint=true and openWorldHint=true, which align with the description. The description adds that the tool returns an 'audited deliverable' and validates inputs server-side, which is useful context. However, it does not disclose behaviors like potential long-running execution (despite an async parameter in the schema) or any limitations.

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 relatively short (3 sentences) but contains some filler like 'Gapup agent-payable C-suite expertise (CRO)' which adds little value. The first sentence largely duplicates the title, and the reference case, while informative, could be expressed more succinctly. Overall, acceptable but not tightly structured.

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?

The tool has a complex schema (nested objects, 5 parameters) and no output schema, so the description should provide a thorough explanation of inputs and outputs. It gives a single reference case but does not describe the full deliverable structure, how to specify period, or the meaning of historical conversion data. This is insufficient 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.

Parameters2/5

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

Schema description coverage is only 20% (only the async parameter is described), so the description must compensate. The reference case mentions '50 deals enterprise/mid-market' and 'forecast confidence par deal', giving some meaning to pipeline structure, but it does not explain key parameters like company, pipeline, historicalConversionByStage, or focus. The instruction to 'send the documented case fields' is vague without actual documentation.

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

Purpose4/5

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

The description clearly states it provides a sales pipeline forecast ('Prévision de pipeline commercial') and returns a structured, audited deliverable. The reference case adds specifics (per-deal confidence, commit/best-case/worst-case), making the purpose unambiguous. However, it does not explicitly differentiate from sibling tools, though the name strongly implies its function.

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 explicit guidance on when to use this tool versus alternatives. The mention of 'send the documented case fields' hints at invocation but does not clarify use cases, prerequisites, or exclusions. The reference case is an example, not 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

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.