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Pepe-MCP-Server

Pepe-MCP-Server

by AldereteSergio
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
Local

deep_research

Executes comprehensive web research from a structured CPQO query, returning a detailed brief with ordered hypotheses and actionable answers for complex problems.

Instructions

Modo investigación amplia (más pasadas/fuentes que search). Mismo CPQO; suele requerir muchos OBJECTIVES (≥6 si el tema es grande). Skill pepe-research-brief.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "query": "<query>"
    },
    "name": "deep_research"
  }
}

Input Schema

NameRequiredDescriptionDefault
queryYesUn solo string CPQO (skill pepe-research-brief): CONTEXT · PROBLEM · QUESTIONS (bullets) · OBJECTIVES (1..n numerados). Opcional SCOPE/NON-GOALS.
attachmentsNoOpcional. Rutas absolutas (datasets, PDFs).

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.4.0

TDQS

A3.5/5.0
Behavior2/5

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

No annotations exist, so the description must carry behavioral disclosure. It mentions more passes/sources than search, but does not describe output format, latency, cost, side effects, or potential warnings. It focuses on input requirements rather than what happens when the tool runs.

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

Conciseness5/5

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

The description is two dense sentences with no filler. It front-loads the core differentiator (`más pasadas/fuentes que search`), then gives actionable input guidance and the skill reference.

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

Completeness3/5

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

For a two-parameter tool, the description covers what it does, when to use it, and the required query structure. However, with no output schema and no annotations, the absence of any statement about the return format or expected deliverable leaves a meaningful gap for an agent.

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 description coverage is 100%, so the baseline is 3. The description adds useful context about the CPQO format and the need for multiple OBJECTIVES, reinforcing the schema's query guidance, but it does not materially extend the schema definitions.

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 identifies the tool as a broad research mode with more passes/sources than `search`, giving a clear functional identity. It lacks a direct verb like 'runs' or 'performs', but the comparison with `search` makes the purpose specific enough to distinguish it.

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?

It explicitly positions the tool relative to `search` ('más pasadas/fuentes que `search`') and gives practical guidance about needing many OBJECTIVES for large topics. It does not state explicit exclusions or when to prefer alternatives like `chat_perplexity`, but the context is clear.

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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