POSTFACT — Resolve Ambiguous Side Effects Before Retry
Server Details
Resolve whether an uncertain side-effecting action completed before software retries it.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- GSterlingPress/postfact-api
- GitHub Stars
- 0
- Server Listing
- postfact-mcp
Glama MCP Gateway
Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.
Full call logging
Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.
Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.
Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Tool Definition Quality
Average 3.9/5 across 1 of 1 tools scored.
With only a single tool, there is no possibility of confusing it with another; the purpose is clear and distinct.
The tool name follows a clean verb_noun pattern ('resolve_outcome'), and with one tool, consistency is trivially maintained.
One tool is minimal but appropriate for the server's narrow, focused purpose. It falls slightly below the typical 3-15 tool range but earns its place.
The tool covers all necessary outcome states (DONE, NOT_DONE, UNKNOWN), providing a complete resolution mechanism for ambiguous side effects with no apparent gaps.
Available Tools
1 toolresolve_outcomeResolve Ambiguous Side EffectARead-onlyIdempotentInspect
Call after a side-effecting API/tool action fails ambiguously and before retrying. POSTFACT returns DONE only with affirmative completion evidence, NOT_DONE only with affirmative non-execution evidence, otherwise UNKNOWN. UNKNOWN is the safe default.
| Name | Required | Description | Default |
|---|---|---|---|
| method | No | ||
| status | No | ||
| failure | No | ||
| evidence | No | ||
| requestId | No | ||
| sideEffect | No | ||
| resourceRef | No | ||
| idempotencyKey | No | ||
| transactionRef | No |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare readOnly, idempotent, and non-destructive. The description adds the decision rule: DONE requires affirmative completion evidence, NOT_DONE requires affirmative non-execution evidence, otherwise UNKNOWN. This provides valuable behavioral context beyond the annotations, with no contradiction.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the when-to-use context, and each word contributes meaning. The reference to 'POSTFACT' is slightly unexplained but does not waste words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With 9 parameters, a nested evidence object, and no output schema, the description is insufficient. It covers the high-level decision logic but omits parameter semantics and return format, leaving an agent with significant gaps in knowing how to construct a request or interpret the response.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 9 parameters with zero description coverage, and the description offers no parameter-level guidance. The mention of 'evidence' is conceptual, not mapped to the evidence object structure. An agent would be unable to correctly populate or interpret parameters like method, status, requestId, or sideEffect.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states that the tool resolves ambiguous side effects after a side-effecting action fails, and specifies the three possible outcomes (DONE, NOT_DONE, UNKNOWN). This makes the purpose specific and distinguishable, even in the absence of sibling tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly provides the trigger condition: call after an ambiguous failure and before retrying. It does not mention when not to use, but the context is sufficient for an agent to decide. No alternative tools are listed, so exclusion criteria are not needed.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
Discussions
No comments yet. Be the first to start the discussion!
Related MCP Servers
- Flicense-qualityCmaintenanceVerifies AI agent actions by exercising public web surfaces and returning evidence-based pass/fail verdicts on real outcomes, rather than trusting self-reported success messages.
- Alicense-qualityBmaintenanceFreshness gate for AI agents: verify a belief is still true against the live world before you act.Apache 2.0
- Alicense-qualityBmaintenanceMCP server that verifies restaurant booking outcomes by reconciling API claims against independent re-reads, with a recovery dial for stakes-based retry policies.MIT
- Flicense-qualityBmaintenanceMCP server that prevents duplicate side effects in AI agents by using idempotency keys and durable receipts, ensuring actions like refunds, emails, or orders execute exactly once even across retries.
Your Connectors
Sign in to create a connector for this server.