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MCP Elasticsearch Demo

MCP Elasticsearch Demo

by dev-com2020
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
Hybrid

Znajdź podobne dokumenty

find_similar

Retrieve documents semantically similar to a given document ID. Use it to find related manuals, videos, or files when you need more items like a specific one.

Instructions

Zwraca dokumenty najbardziej semantycznie podobne do podanego (po id) — np. "pokaż więcej takich jak ten".

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": {
      "document_id": "<document_id>"
    },
    "name": "find_similar"
  }
}

Input Schema

NameRequiredDescriptionDefault
top_kNoLiczba wyników (domyślnie 5)
document_idYesIdentyfikator dokumentu bazowego

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A3.5/5.0
Behavior3/5

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

With no annotations, the description carries the full behavioral burden. It usefully discloses the ranking semantics ('najbardziej semantycznie podobne'), so the agent knows results are ordered by semantic closeness, but it says nothing about read-only safety, permissions, pagination, or result count limits beyond what the schema shows.

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?

A single, front-loaded sentence that leads with the operation and its ranking behavior, with the usage example appended. No filler, though it is terse enough that it could afford one more clause of guidance.

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?

For a two-parameter read tool with no output schema and simple structure, the description covers what is returned and how it is ranked, which is largely sufficient. The count/default behavior lives in the schema, so little is genuinely missing.

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 both document_id and top_k are already documented in the schema. The description only echoes the 'po id' keying for document_id and adds no format or constraint detail beyond it, so the baseline 3 applies.

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 states a specific verb and resource ('Zwraca dokumenty najbardziej semantycznie podobne') plus the keying mechanism ('po id'), so an agent knows exactly what it retrieves. It is clear on its own, but it does not name or contrast itself with siblings like search_documents or get_document, leaving differentiation implicit.

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 example 'pokaż więcej takich jak ten' implies the intended usage pattern (find-more-like-this rather than a keyword search), which is helpful. However, it never states when to prefer this over search_documents or how it differs from get_document, and gives no when-not 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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