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find_conversations

Find recent public posts where people ask for a tool, an alternative, or help with a job, so you can decide whether an honest, disclosed reply would help them. Remote server covers Stack Exchange (incl. softwarerecs) and Bluesky; for Hacker News + GitHub run the local MCP: npx -y https://askhound.haullabs.dev/askhound.tgz mcp. Returns url, title, snippet, date, source and required attribution. Never auto-post replies.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
daysNo
limitNo
queryYesPlain phrase, e.g. "otter alternative" or "transcript export"
contextNoAt least one must appear (kills noise), e.g. ["meeting","transcript"]
excludeNo
sourcesNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does well: it discloses the source coverage, the return fields (url, title, snippet, date, source, attribution), and a key safety boundary ('Never auto-post replies'), implying a read-only search. It doesn't mention rate limits, auth requirements, or result pagination, which keeps it below 5.

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 purpose in the first clause, then layers routing and return info efficiently. The embedded npx install string is long but functional and necessary. Overall dense but each sentence earns its place.

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 read-only discovery tool with no output schema, the description lists the returned fields and explains multi-source routing, which is what an agent needs to interpret results. The remaining gap is parameter-level detail that the sparse schema does not supply.

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 only 33% (only query and context are documented) and the description adds no parameter-level meaning — days, limit, exclude, and sources are never explained. The source routing sentence hints at the 'sources' enum but does not describe how to use the parameter, so the description fails to compensate for the coverage gap.

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 ('Find') plus a tightly scoped resource ('recent public posts where people ask for a tool, an alternative, or help with a job'), including the qualifying intent of the posts. This is clearly distinguishable from the sibling tools (query building, reply policy, intent packs), so an agent knows exactly what this retrieves without opening the schema.

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?

The description gives explicit downstream context ('so you can decide whether an honest, disclosed reply would help them') and a concrete routing rule: remote covers Stack Exchange/Bluesky, while Hacker News + GitHub require the local MCP invocation. It stops short of naming a sibling as an alternative or stating when NOT to call it, but the when-to-use signal is clear.

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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