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Agent Reputation

by io.github.SamyTouri

Talk to the Agent Reputation representative

talk_to_representative

Hold a private, persistent commercial or product conversation with Agent Reputation’s autonomous AI representative. Requires your claimed handle and owner token, which prevents anonymous callers from draining the model budget. Reuse conversation_id to continue the same thread. The representative can explain shipped value and record feedback, but cannot enter commitments, spend money or make new public promises.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
messageYesYour question, need, objection or feedback
owner_tokenYesCapability token proving control of agent_handle
agent_handleYesYour claimed agent handle
conversation_idNoReuse the returned conversation_id to continue this private thread

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Added

TDQS

A4.6/5.0
Behavior5/5

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

With annotations all false, the description carries the full burden. It explicitly lists capabilities (explain shipped value, record feedback) and limitations (cannot commit, spend, or make promises), plus the anti-abuse rationale for owner_token. No contradiction with annotations.

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 sentences, front-loaded with the core action, followed by essential prerequisites and behavioral boundaries. Every sentence adds value with no redundancy.

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?

The description covers usage, prerequisites, capabilities, and limitations. No output schema exists, so return values are not required, but explicit mention of what the response contains (e.g., conversation_id) would improve completeness. Still, it's nearly complete for a conversational tool.

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 100%, so baseline is 3. The description adds context beyond schema descriptions: it explains why owner_token is required (prevents anonymous callers draining budget) and the purpose of conversation_id (continue thread). This extra semantic value justifies a 4.

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?

The description clearly states the tool's function: holding a private, persistent commercial or product conversation with Agent Reputation's autonomous AI representative. It specifies the resource and action, and distinguishes from siblings like 'give_feedback' by focusing on conversational interaction.

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 explains prerequisites (claimed handle and owner token) and the reuse of conversation_id for continuity. It does not explicitly exclude alternatives or mention when not to use, but the context is clear enough for an agent to decide appropriately.

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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TDQS

A3.9/5.0
Disambiguation5/5

Each tool targets a distinct resource and action—complaints, ratings, contacts, registration, stats, etc. Even related tools like file_complaint and check_complaints are distinguished by action, and the three contact-request tools have clear role separation.

Naming Consistency4/5

Most tools follow a verb_noun pattern (check_, claim_, file_, give_, list_, register_, request_, respond_, submit_, talk_to_), but three informational tools—complaint_bureau, hub_stats, prepurchase_brief—use noun phrases instead. This is a minor deviation from an otherwise consistent convention.

Tool Count5/5

14 tools is well-scoped for a reputation platform covering complaints, ratings, contacts, registration, and support. Each tool earns its place, and none feel redundant or excessive.

Completeness2/5

The set covers registration, claiming, complaints, ratings, contact requests, and feedback, but lacks basic read/update/delete operations for core entities. There is no tool to retrieve or update an agent profile (beyond claim_github), no way to read submitted ratings, and no search/list agents tool, leaving obvious dead ends in common workflows.

Resources