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jtkehler

crowdsmart-mcp

by jtkehler

get_jam_responses

Read-only

Retrieve each Knowledge Agent's response in a Jam—including author ID, probability, text, and citations—and filter by prompt order to review what agents said.

Instructions

Read the responses in a Jam: who wrote each one, its probability, text and citations.

Use it to see what each Knowledge Agent said. "author_id" is the agent's ID. A citation such as [1] in the text is citation 1 of the response: read its document with read_document(author_id, document_id). The seed ideas of the Jam's plan have the author_type "seed". "probability" is the author's estimate, for a prediction prompt.

Args: jam_id: Jam ID. prompt_order: Only the responses to this prompt (the first is 0), from get_jam.

Input Schema

NameRequiredDescriptionDefault
jam_idYes
prompt_orderNo

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.9/5.0
Behavior4/5

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

readOnlyHint=true already tells the agent this is a safe read, and the description adds real behavioral context beyond that: author_type 'seed' for plan seed ideas, the meaning of [n] citation markers, and that probability is the author's estimate for prediction prompts. It does not mention pagination or size limits on the response set.

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?

Purpose and return shape are front-loaded in the first sentence, with secondary semantics (citations, seed authors, probability) after. The citation/read_document aside is slightly tangential but earns its place by preventing misuse.

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?

With an output schema present, the description needn't explain the return shape, and it still clarifies the semantic meaning of fields (probability, author_type, citation markers). Only the thin coverage of jam_id keeps it from being fully complete.

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 0%, so the description carries the burden. It explains prompt_order well ('the first is 0', scoped to a prompt, sourced from get_jam) but jam_id gets only a trivial gloss ('Jam ID'), leaving format expectations unstated beyond the schema pattern.

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 opens with a specific verb and resource ('Read the responses in a Jam') and enumerates exactly what is returned: author, probability, text, citations. It does not explicitly contrast itself with the closely named sibling get_jam_results, so an agent still has to infer that difference.

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

Usage Guidelines4/5

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

'Use it to see what each Knowledge Agent said' gives a clear usage context, and it routes the agent elsewhere for two related needs ('from get_jam' for prompt ordering, read_document for citations). It stops short of naming an alternative tool or stating when not to use it.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.