Findable
Server Details
Public website audits and sourced SEO/AEO content drafts, ready for your review.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP · MCP 2025-11-25
- URL
TDQS
Scored across 2 tools
start_audit initiates an audit and returns an ID, while check_audit polls status and retrieves the completed report. The two tools have clearly distinct purposes with no overlap.
Both tools follow the same verb_noun snake_case pattern: start_audit and check_audit. The naming is predictable and consistent throughout.
Only two tools are provided for a service that creates and stores audits. While the core start-and-poll workflow is covered, the surface feels thin without list, cancel, or delete operations.
The core async lifecycle is complete: start an audit and check its result. Minor gaps exist, such as no way to list, cancel, or delete audits, but these are not essential for the stated purpose.
Available Tools
2 toolscheck_auditCheck a Website SEO/AEO auditBRead-onlyIdempotentInspect
Where a Website SEO/AEO audit stands. When it is ready, the full result as text and a link to it on Findable's site.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | The id start returned. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly, idempotent, non-destructive, non-open-world behavior, so safety is covered. The description adds genuine value by disclosing the completion payload (full result as text plus a Findable link), but says nothing about the pending state, intermediate status values, or error behavior for a bad id.
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?
Two short sentences with no filler, and the return-value information is front-loaded after the purpose. The opening clause is a verbless fragment, which costs a little clarity but not length.
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 no output schema, the description carries the return-value burden; it covers the ready state but not the pending/in-progress state an agent polling this tool will most often encounter. For a simple one-param read tool it is nearly adequate, but the missing in-progress semantics leaves a real 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?
There is a single required parameter and schema description coverage is 100% ("The id start returned."), so the schema already carries the semantics. The description adds nothing about the id's origin, format, or behavior for an invalid/unknown id, so baseline 3 applies.
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 identifies the resource (a Website SEO/AEO audit) and the operation (reading its current state, and the full result once ready). That is enough to separate it from start_audit, though the verb is expressed as a fragment ("Where ... stands") rather than an explicit 'check/poll status'.
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?
Usage is only implied: by describing a return that appears "when it is ready", it hints at polling after start_audit, but it never states when to call this versus start_audit, how often to poll, or what to do while the audit is pending.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
start_auditStart a Website SEO/AEO auditAInspect
Create a stored SEO/AEO audit of a public business website without an account or data connection. Accepts optional dated provider reports. Reads the homepage, up to four internal pages, robots.txt, sitemap.xml and up to five relevant competitor websites discovered from public market research or supplied by the client. Samples two unbranded customer questions through live OpenAI API web searches when available. Newly audited competitors use the same questions and source-check rubric. Recent public research may be reused with its original dates; private client data is excluded from that cache. Reports include sourced public business contacts for manual review; the service does not draft or send outreach. Returns an audit ID for retrieving progress and the completed report. Reports include sourced findings, exact target URLs, up to three priorities, relevant content drafts, expected impact, realistic review points and measurement steps. The service cannot access other MCP connections, infer private metrics or edit websites.
Starts the work and returns an id. It usually takes several minutes.
| Name | Required | Description | Default |
|---|---|---|---|
| site | Yes | The client's public HTTPS website, such as https://lightsource.ai/. | |
| goals | No | The target customer and desired customer action, when known. | |
| hosting | No | Where the client publishes the website, such as Framer, Webflow or WordPress. | |
| shared_data | No | Optional snapshots collected with the client's permission through their own tools. Snapshots contain report data rather than credentials. The audit can run without them. | |
| priority_urls | No | Up to three priority pages on the client's website. These replace the automatic page selection. | |
| competitor_urls | No | Optional public competitor websites. Findable checks relevance to the same buyers and market before comparing them. | |
| research_competitors | No | Whether the public audit includes relevant competitor discovery and comparisons. Defaults to true. | |
| refresh_public_research | No | Fetch fresh public evidence and analysis even when recent research exists, for example after website changes. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Against annotations that only flag readOnly=false, openWorld=true, idempotent=false, the description adds substantial behavioral context: what pages and files are crawled, that two unbranded customer questions are sampled via live OpenAI API searches, how public research is cached and dated, that private client data is excluded, and explicit negative capabilities (cannot access other MCP connections, infer private metrics, or edit websites). It even states the service does not draft or send outreach.
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 scoping sentence is front-loaded correctly, but the body runs long and repeats itself: report contents are described twice ("Reports include sourced public business contacts..." and "Reports include sourced findings, exact target URLs..."), and the final line restates the already-stated id return. The density is partly justified by the tool's complexity, but several sentences could be merged without losing information.
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 no output schema and complex crawler side effects, the description carries the full burden and does so thoroughly: it explains the returned audit ID, what the eventual report contains, the runtime expectation, caching policy, and hard capability limits. An agent has everything needed to invoke it and set expectations correctly.
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 coverage is 100%, so the schema already documents every parameter, which sets a baseline of 3. The description adds intent beyond the schema, clarifying that shared_data consists of optional dated provider reports, that competitors are discovered via public market research or supplied by the client, and that reused public research keeps its original dates while private data is excluded from the cache.
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 opening sentence gives a specific verb and resource ("Create a stored SEO/AEO audit of a public business website") and then scopes it precisely: no account, no data connection required. The description details exactly what is read (homepage, up to four internal pages, robots.txt, sitemap.xml, competitors) and that an audit ID is returned, making it unmistakably distinct from the check_audit sibling.
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 establishes context well, stating that shared snapshots are optional ("The audit can run without them") and that the call returns an id and "usually takes several minutes," which implicitly routes the agent to a polling sibling. It never explicitly names check_audit or states when not to start a new audit, so it stops short of full when/when-not guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
2 tool updates
- First observed
check_audit - First observed
start_audit
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