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TextLite MCP Server

TextLite MCP Server

Official
by Mantri-AI
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

Semantic Search

semantic_search

Rank candidate texts by cosine similarity to a query and return the top-k matches for relevance-based retrieval.

Instructions

Return top-k candidate texts ordered by cosine similarity to a query.

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>",
      "texts": "<texts>"
    },
    "name": "semantic_search"
  }
}

Input Schema

NameRequiredDescriptionDefault
queryYes
textsYes
top_kNo

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.9/5.0
Behavior2/5

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

No annotations are supplied, so the description carries the full behavioral burden. It states the ordering criterion but not the similarity metric's model/embedding assumptions, whether scores are returned alongside texts, what happens when top_k exceeds the corpus size, or the cost/latency profile of encoding many texts.

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 efficient sentence with the ranking criterion front-loaded and no wasteful wording. It is arguably too terse for a tool with zero annotation and schema-description support, but there is no padding to trim.

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?

An output schema exists, so return-value shape needn't be explained, but with no annotations and 0% parameter coverage the description leaves key invocation details unstated. For a retrieval primitive whose results feed other tools, the missing corpus-vs-query framing and top_k semantics are real gaps.

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 0% across three parameters, so the description must compensate and does not. It mentions 'query', 'texts', and 'top-k' only in passing prose, never clarifying that texts is the corpus to search, that top_k defaults to 5, or any bounds on top_k.

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?

States a specific verb (return), resource (candidate texts), and ranking criterion (cosine similarity to a query), plus the top-k scope. This distinguishes it from siblings like semantic_similarity and rerank_passages, which imply pairwise scoring or reordering of supplied results rather than retrieval by similarity.

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?

The description explains the mechanism but gives no when-to-use guidance: it never says to prefer this over semantic_similarity or rerank_passages, nor when a caller should reach for it at all. No exclusions, prerequisites, or alternative-routing hints are present.

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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