Returns TESSA's portfolio of project results.
Optional filters:
industry: industry name keyword (case-insensitive contains)
service_slug: filter to studies that used a given service
| Name | Required | Description | Default |
|---|---|---|---|
| industry | No | ||
| service_slug | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of disclosure. It states the read-only nature via 'Returns' and clarifies filter behavior (case-insensitive contains). However, it omits details like return structure, pagination, or auth requirements, leaving some behavioral aspects undisclosed.
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 extremely concise, opening with the core action and resource, then using a compact bullet list for filters. Every sentence earns its place with no fluff or redundancy.
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 read tool with no output schema and only two optional parameters, the description covers the essential purpose and filter semantics. It lacks explicit comparison to siblings or return format details, but the tool's simplicity means this is not a critical gap.
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?
Schema description coverage is 0%, so the description must compensate. It fully explains both parameters: 'industry' as a keyword with case-insensitive containment and 'service_slug' as a filter for studies using a given service. This exceeds the schema's bare type info and provides clear meaning.
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 the action ('Returns') and the resource ('TESSA's portfolio of project results'), which distinguishes it from sibling tools that focus on services or firm profiles. However, it does not explicitly differentiate itself from siblings by name, so it misses the top score.
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 gives clear context by stating the tool returns a portfolio of project results and lists optional filters. It implies when to use it (when needing case studies), though it does not mention exclusions or alternative tools. This meets the 'clear context, no exclusions' level.
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.
Each tool has a clear, distinct purpose. The get_* tools retrieve different data types (services, firm profiles, case studies, audit offerings), the request_* tools target different actions (introduction, quote, strategy session), and assess_ai_readiness and claim_listing are unique. No two tools appear to do the same thing.
Tool names are all snake_case and follow a verb_noun structure, but the verbs vary (assess, claim, find, get, request) rather than using a single consistent pattern. The get_ and request_ subgroups are internally consistent, so the naming is readable and predictable despite the variety.
With 10 tools, the server is well-scoped. Each tool serves a clear function in the marketing/directory domain: discovery (find, get), engagement (request, claim), and assessment (assess, get_wcag_audit). The count is neither sparse nor overwhelming.
The surface covers the core workflows: searching the directory, retrieving firm details and services, requesting intros/quotes/sessions, claiming listings, and checking AI readiness. Minor gaps exist (e.g., no tool to update a listing or access the compliance registry directly), but these are not likely to cause agent failures.