get_anomaly
Read-only
Get an anomaly
One anomaly by its stable content-derived key.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | The stable anomaly key. |
PeppolStatus
TDQS
A3.9/5.0
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The readOnlyHint annotation already indicates a safe read operation, and the description's 'Get' is consistent with that. The description adds little behavioral context beyond what annotations provide, but there is 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is only two sentences, front-loaded with the tool's purpose, and contains no unnecessary words or repetitive information.
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 simple read-by-id tool with one parameter, a readOnlyHint, and no output schema, this description is complete. It conveys the essential purpose and the key concept without needing extra detail.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema fully documents the 'id' parameter as 'The stable anomaly key.' The description adds 'content-derived' to clarify the key's origin, but this is a minor addition given the schema's already high coverage.
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 action ('Get an anomaly') and resource ('anomaly'), and specifies it retrieves a single anomaly by its stable content-derived key. This distinguishes it from sibling list_anomalies and other get_* tools.
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?
The description implies usage when you have a known stable key for a specific anomaly, but it does not explicitly mention alternatives like list_anomalies for browsing or searching. The guidance is implied rather than explicit.
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
A3.8/5.0
Disambiguation4/5
Most tools are cleanly separated by resource type: participants, access points, hosts, providers, incidents, anomalies, and SLA each have their own get/list vocabulary. The main ambiguous pairs are get_provider_sla vs get_provider_sla_by_key, list_providers vs list_public_providers, and get_summary vs get_network_summary.
Naming Consistency4/5
The overall get_/list_ verb_noun pattern is consistent and readable, and plural/singular resource names are mostly clear. There are a few exceptions: get_provider_sla and get_country_providers return collections despite using get_, and list_provider_certs is more of an aggregate posture endpoint than a simple list.
Tool Count2/5
43 tools is well beyond the typical well-scoped MCP surface and will make the tool set harder for an agent to navigate defensibly. The tools are systematically grouped, but this looks like a broad REST API surface rather than a compact, purpose-fit MCP server.
Completeness5/5
For a read-only monitoring and directory domain, the coverage is unusually complete: list/detail endpoints, histories, SLA tables, churn breakdowns, anomalies, incidents, adoption aggregates, software landscape, and quality checks are all represented. The drill-down routes such as churn totals to churn participants also avoid dead ends.