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mohisyed

jPOS MCP Server

by mohisyed

docs_search_jpos

Find precise answers in jPOS documentation by combining semantic and keyword search, returning up to 5 matched sections with similarity scores.

Instructions

Search jPOS documentation using hybrid semantic + keyword search.

Returns up to 5 matches with source, section, page, and similarity score. Scores are calibrated for the all-mpnet-base-v2 embedding model combined with a keyword overlap rerank:

  • strong >= 0.55 (direct answer expected in chunk)

  • good >= 0.40 (relevant context, may need synthesis)

  • partial >= 0.25 (tangentially related) Chunks below 0.25 are filtered out as noise.

Pipeline:

  1. Expand short/jargon queries with domain context

  2. Embed the expanded query and fetch top 15 candidates by cosine

  3. Rerank candidates with keyword overlap from the original query (70% cosine + 30% keyword overlap)

  4. Return top 5

Why return raw chunks instead of summarizing: the calling LLM can reason about conflicting chunks, notice version differences, and assess confidence from similarity scores. Pre-summarizing loses this nuance.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesNatural language question about jPOS configuration or usage.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

No annotations provided, so description carries full burden. It comprehensively discloses the retrieval pipeline (query expansion, embedding, reranking), score calibration thresholds, and the rationale for returning raw chunks instead of summaries.

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?

The description is well-structured with a clear front-loaded purpose, followed by return format, score calibration, pipeline, and rationale. Each sentence provides useful information, though the pipeline detail may be slightly verbose.

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?

Given the tool's simplicity (one parameter, no output schema shown), the description covers query processing, scoring, and return format. It could mention potential errors or rate limits, but overall is fairly complete.

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

Parameters4/5

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

Schema coverage is 100% with a clear parameter description. The tool description adds value by explaining how the query is expanded and used in the pipeline, going beyond the schema's basic description.

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?

The description clearly states the tool's purpose: 'Search jPOS documentation using hybrid semantic + keyword search.' It specifies the resource (jPOS documentation) and action (search), and is distinct from sibling tools like iso_decode_mti or msg_build_message.

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

Usage Guidelines3/5

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

The description implies usage for searching documentation but does not explicitly state when to use this tool versus alternatives or provide any when-not guidance. No mention of other tools or conditions.

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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