error_detailOne error group in full by fingerprint (from list_errors): every field, plus the most recent redacted occurrences — status, connector, job id, workspace, and a shape-only echo of the inputs. This is what makes a bug reproducible. Read-only, 0 credits.
| Name | Required | Description | Default |
|---|---|---|---|
| fingerprint | Yes | the `fp` value from list_errors |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses that the tool is read-only and costs 0 credits, and that occurrences are redacted, adding behavioral context beyond the annotations which already set readOnlyHint=true and destructiveHint=false. It does not contradict annotations and provides useful details about data privacy and cost.
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-loads the core action ('One error group in full by fingerprint'), and packs essential details (fields, redaction, input echo, read-only, 0 credits) without waste. Every phrase earns its place.
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?
For a simple, one-parameter read-only tool with no output schema, the description sufficiently conveys what is returned, the provenance of the fingerprint, and cost implications. It lacks only minor details like handling of missing fingerprints, but these are not essential for correct invocation.
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 fully documents the single parameter 'fingerprint' with its source ('the fp value from list_errors'), so schema coverage is 100%. The description only mirrors this by saying 'by fingerprint (from list_errors)' without adding additional meaning, staying at the baseline for high schema coverage.
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 it retrieves a single error group by fingerprint, enumerates the content (every field plus redacted occurrences with status, connector, job id, workspace, and input echo), and explicitly references the sibling list_errors as the source. This distinguishes it from listing tools and other detail tools, leaving no ambiguity about its function.
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?
It implies usage by referencing 'from list_errors' and framing the result as 'what makes a bug reproducible', suggesting it is used for debugging after obtaining a fingerprint. However, it does not explicitly state when to use it versus alternatives or mention when not to use it, lacking an explicit routing rule.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
With 293 tools, the surface is enormous and many tools have overlapping purposes—multiple posting tools (post_to_meta, post_to_linkedin, schedule_post, etc.), multiple analytics tools per channel, and several search tools (search_meta_ads, search_instagram, search_reddit...). While each description is detailed, the volume makes it difficult for an agent to reliably distinguish between similar tools without careful reading, leading to frequent misselection.
The naming is largely consistent with a verb_noun pattern (post_to_*, list_*, create_*, delete_*, update_*, manage_*). There are clear families for major operations. A few outliers like 'google_business_account', 'hermoso_capabilities', and 'store_get' break the pattern, but the overwhelming majority follow a predictable structure, making navigation somewhat easier.
293 tools is far beyond any reasonable scope for a single MCP server, even for a comprehensive marketing platform. The calibration guide flags 50+ as an extreme mismatch, and this is nearly six times that threshold. Such a large surface overwhelms context windows, increases the probability of misselection, and makes it impractical for agents to learn or use effectively.
The tool set covers a vast domain: ad creation and rendering, posting across nine+ social channels, analytics and reporting, file management (Drive/OneDrive), competitor research, brand management, and more. It appears to provide CRUD and lifecycle coverage for most resources. While there may be minor gaps given the immense scope, the overall coverage is impressively comprehensive.