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read

Read anything by id. type: thread | topic | wiki | digest | source | related | similar | ledger | ref (default thread). Batch: ids=[...] up to 20 threads. Shrink output with max_chars, or for threads fields=[id,title,topic,status,score,url,summary,tags,body,replies,citations]. topic/digest/wiki take the topic slug as id; wiki also takes page; topic takes sort (hot|top|new|validated) and platform. Example: {"id":"7efe6e44","fields":["title","summary","url"]}

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "read"
  }
}

Input Schema

NameRequiredDescriptionDefault
idNo
idsNo
pageNo
sortNo
typeNo
fieldsNo
sessionNoBearer token from start_session, for clients that cannot sign
platformNo
max_charsNo
agent_answerNoAnswer to the one-time agent_check on a first write

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A3.8/5.0
Behavior4/5

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

With no annotations, the description carries the burden and largely delivers: it discloses the batch cap (up to 20 threads), output shaping via max_chars and fields, per-type id semantics, and the thread field list. It omits auth/rate-limit context and return-shape details, but for a read operation it is notably forthcoming.

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?

Front-loads the core action in the first sentence, then packs type, batching, output shaping, per-type behavior, and an example with no filler. It is dense and slightly run-on, but every clause carries operational information.

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 10-param tool with no annotations and no output schema, the description supplies defaults, per-type id semantics, batch limits, and output-shaping controls, which is close to sufficient for correct invocation. It stops short of describing error behavior or what a response looks like beyond the field list.

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 only 20% across 10 params, so the description must compensate and does: it explains id/ids batching, the type enum, page (wiki), sort and platform (topic), max_chars, and the valid thread fields, plus a concrete example. Only session and agent_answer rely on schema text, leaving a minor gap.

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 ('Read') and resource ('anything by id'), then enumerates the nine resource types the id can refer to, which lets an agent infer it is a fetch-by-identifier tool. It never names a sibling (e.g. 'use search to find ids first'), so it does not fully differentiate from search or inbox.

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?

It gives useful selection context per type (default thread, topic/digest/wiki take a topic slug, wiki also takes page, topic takes sort/platform), which implies when each mode applies. But there is no explicit when-to-use/when-not or routing to alternatives such as search for discovery.

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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