Skip to main content
Glama
Sign In
agent-passport-system-mcp

agent-passport-system-mcp

by agent-passport-system
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript

Update Agent Reputation

update_reputation

Adjust an agent's reputation after a task result: success increases trust and reduces uncertainty; failure does the opposite, scaled by evidence class.

Instructions

Update an agent's reputation after a task result. Success increases mu and decreases sigma, and failure does the opposite. Higher evidence class = larger effect.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "agentId": "<agentId>",
      "principalId": "<principalId>",
      "scope": "<scope>",
      "success": "<success>",
      "evidenceClass": "<evidenceClass>"
    },
    "name": "update_reputation"
  }
}

Input Schema

NameRequiredDescriptionDefault
scopeYesReputation scope
agentIdYesAgent whose reputation to update
successYesWhether the task succeeded
principalIdYesPrincipal ID
evidenceClassYesComplexity of the task

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv4.0.0
    • changedInput schema / $schema
      Previous value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"
    • removedInput schema / additionalProperties
      Removed value: -false
  2. Addedv2.8.0

TDQS

A3.9/5.0
Behavior4/5

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

Annotations already declare the safety profile (readOnlyHint=false, idempotentHint=false, destructiveHint=false), so the bar is lower, and the description still adds real behavioral substance: the direction of effect (mu up / sigma down on success, reversed on failure) and the scaling rule for evidence class. It omits permissions/auth requirements, whether effects accumulate across calls, and any rate or volume constraints.

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 short sentences, zero filler, and the core action plus its trigger are front-loaded before the model mechanics. Every sentence carries distinct 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?

Given five required parameters (all schema-documented), rich annotations, and no output schema, the description covers purpose, mechanism, and trigger adequately. The one gap is what the call returns — the new mu/sigma values or just an acknowledgement — which the agent cannot learn from anywhere else since no output schema exists.

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 the baseline is 3, but the description goes beyond the schema by explaining what the 'success' flag does to the underlying estimate and by clarifying that 'evidenceClass' scales the magnitude of the effect rather than merely labeling task complexity. It adds nothing about 'scope' or 'principalId', which are the two least self-explanatory fields.

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 uses a specific verb+resource ('Update an agent's reputation') and adds a clear trigger scope ('after a task result'), which separates it from endorsement-style siblings like vouch_reputation or apply_reputation_downgrade. It does not explicitly name any sibling, so the differentiation is implied rather than stated.

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?

Usage context is present but implied: 'after a task result' tells the agent the general moment to call it, and success/failure framing suggests it is outcome-driven. It never says when not to use it or how it differs from adjacent reputation tools (vouch_reputation, apply_reputation_downgrade), leaving the agent to infer the boundary.

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

Deploy Server

Other Tools