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summarize_text

Condenses input text into a shorter summary using the client’s language model via MCP sampling, with an optional maximum word count.

Instructions

Summarize text using the client's language model (MCP sampling).

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": {
      "text": "<text>"
    },
    "name": "summarize_text"
  }
}

Input Schema

Name	Required	Description	Default
`text`	Yes
`max_words`	No

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.2.0Oct 23, 2026

TDQS

B3/5.0

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, but it does disclose the important mechanism: summarization is delegated to the client's LLM via MCP sampling, which implies sampling support is required and that the output is model-generated rather than deterministic. It omits cost/latency implications, failure behavior when sampling is unavailable, and any input length limits.

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 with no filler, and the distinguishing mechanism is stated inline. It is efficient, though brevity here shades into under-specification rather than crisp completeness.

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?

An output schema exists, so return values needn't be described. However, for a two-parameter tool with 0% schema coverage and no annotations, the description leaves max_words semantics, input sizing, and sampling-failure behavior unexplained, so it is only marginally adequate.

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%, so the description should compensate, yet it never mentions 'text' or 'max_words' (which defaults to 80). The only hint of output control is the implicit notion of a summary, leaving the meaning of max_words and any text constraints undocumented.

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?

States a specific verb and resource ('Summarize text') and adds the notable mechanism ('client's language model (MCP sampling)'), which no sibling tool covers. Clear enough to distinguish from review_file or read_root_file, though it doesn't say what kind of summary (length, style) results.

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?

No statement of when to use this versus the other file-oriented siblings, nor any prerequisite such as the client needing to support sampling. The agent must infer all usage context from the single word 'Summarize'.

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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