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Noemic

by app.noemic
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Find relevant counterparties

find_counterparties
Read-onlyIdempotent

Secondary, read-only research for when the user explicitly asks to find a person for a concrete falsifiable forecast or disagreement, optionally anchored to an existing draft offer. Do not call proactively: the default wagering path recommends open wagers or useful questions instead of people. Results cite only scope-authorized evidence behind each match; weak results are clearly labeled.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
offerIdNo
sessionTokenYesThe private session token returned by finish_sign_in. Reuse it privately and never show it to the user.
conversationContextYesOnly the relevant context from this conversation. Do not claim or imply that Noemic can read any conversation that was not supplied in this call.
explicitUserRequestYesConfirm that the user directly asked to search for counterparties.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changed
    • addedInput schema / properties / explicitUserRequest / const
      Added value: +true
    • changedInput schema / properties / explicitUserRequest / description
      Previous value: -"True only when the user directly asked to search for counterparties. Leave false for model-initiated ambient checks."New value: +"Confirm that the user directly asked to search for counterparties."
    • changedInput schema / required
      Previous value: -[
-  "sessionToken",
-  "conversationContext"
-]New value: +[
+  "sessionToken",
+  "conversationContext",
+  "explicitUserRequest"
+]
  2. Added

TDQS

A4.7/5.0
Behavior4/5

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

Annotations already establish read-only, idempotent, non-destructive behavior. The description adds value by disclosing that results cite only scope-authorized evidence and that weak results are clearly labeled, which is useful beyond what annotations state.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three compact sentences deliver the essential context in order: what the tool is, when it must not be called, and what the results look like. There is no filler or unnecessary repetition of schema fields.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a read-only research tool with no output schema, the description covers selection criteria, invocation timing, optional offer anchoring, and output behavior. The annotations and parameter schemas fill the remaining safety and input requirements, so the agent has enough to invoke it correctly.

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?

The description adds meaning to offerId by calling it an optional anchor to an existing draft offer and connects explicitUserRequest to the stated requirement that the user explicitly asked. Limit is self-explanatory from its min/max constraints, and sessionToken/conversationContext are already described in the schema.

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 action and resource: find a person for a concrete falsifiable forecast or disagreement, optionally anchored to an existing draft offer. It clearly distinguishes itself from siblings like find_relevant_bets by framing the task as people-finding rather than bet-finding.

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

Usage Guidelines5/5

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

Provides explicit trigger conditions: only when the user explicitly asks to find a person for a concrete falsifiable forecast or disagreement. It also gives a strong negative rule ('Do not call proactively') and points to the default alternative path, so the agent knows when not to use this tool.

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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