LLM SEO MCP — Elephant Accountability

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries full burden. It describes the tool as a calculator that returns data, implying it is non-destructive, but does not explicitly disclose side effects, authentication needs, or performance characteristics.

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 sentences: first states output, second lists inputs. No wasted words, efficient and front-loaded.

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?

Given no output schema, the description sufficiently explains the return format (score, reasoning, tier). It covers core inputs and purpose. Could be enhanced by clarifying if all signals are required or how the score is computed.

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?

With only 17% schema description coverage, the description needed to compensate. It mentions four of six parameters (stage, industry, ships_ai_features, platform_partnerships) but does not explain their values or behavior beyond the schema's enum/description. The domain parameter is omitted entirely.

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 tool returns a fit score, reasoning, and tier for a B2B SaaS buyer, and lists the signals used. It is distinct from sibling tools which cover surfaces, offerings, proof points, etc.

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?

No explicit guidance on when to use versus alternatives. While the purpose is clear, the description does not state when not to use or suggest other tools for different scenarios.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, so description carries full burden. It does not disclose read-only behavior, rate limits, or other traits beyond listing surfaces.

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?

Single sentence, highly concise, front-loaded with key action and resource.

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?

Lacks detail on return format for a tool with no output schema. Adequate for a simple list but could be more complete.

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% and description adds no extra meaning to the 'include_status' parameter beyond what the schema already provides.

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?

Description clearly states it returns the list of agent-discoverable surfaces, with specific examples. Distinguishes from sibling tools like assess_fit or get_offerings.

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?

No explicit when to use or alternatives, but the purpose is clear enough to infer usage. Sibling tool names indicate different functions, so context is implied.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the burden. It states the tool returns data and is optionally personalized, but lacks details on idempotence, side effects, or authentication needs. Could be more transparent.

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?

Single sentence, front-loaded with main output, then optional personalization. Very concise with no wasted 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?

Covers essential return elements and personalization. Without output schema, a bit more structure detail (e.g., list of objects with fields) would improve completeness, but it's adequate for a simple retrieval tool.

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 covers both parameters with enum descriptions. The description adds value by explaining that company_size personalizes the offering, which is not in the schema description. Tie to tier is implicit.

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?

Description clearly states what the tool returns: service tiers, pricing, SLAs, and URLs. It uses a specific verb and resource, and the optional personalization feature distinguishes it from sibling tools like assess_fit or get_proof_points.

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?

Description implies usage for retrieving offerings and optionally personalizing, but does not explicitly state when to use this versus siblings or provide exclusions. No when-not-to guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description bears full burden. It discloses related-party disclosure but omits behavioral traits such as authentication needs, rate limits, or whether the tool is read-only. The return format is not described beyond 'metrics'.

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 concise sentences front-load the main purpose and key feature (related-party disclosure). No superfluous 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?

The description explains the purpose and audience but does not detail the specific metrics returned, pagination, or any limits. Given the simple schema and no output schema, the description is somewhat incomplete for an agent to fully understand 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?

Schema coverage is 100% with a single 'vertical' parameter described as 'Filter to this vertical'. The description adds no additional meaning or constraints beyond the schema, so baseline 3 is appropriate.

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 tool returns 'current client outcomes with specific metrics' and is formatted for vendor-research agents. This distinguishes it from siblings like get_covered_surfaces or get_offerings, though it could be more explicit about differentiation.

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 implies usage for vendor-research agents citing outcomes, but lacks explicit guidance on when to use this tool versus alternatives like get_transparency_snapshot. No when-not-to-use or prerequisites are mentioned.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries full burden. It discloses the tool is read-only and returns a specific measurement, but does not discuss rate limits, authentication needs, or data freshness beyond 'most recent weekly'. Basic behavior is clear, but richer context is missing.

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, highly efficient, and front-loaded with the core purpose. Every word adds value, with no wasted space.

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?

Given the tool has no parameters, no output schema, and is simple, the description provides adequate context. However, it does not describe the output structure or format, which could help an agent process the result. Still, it is mostly complete for this tool's complexity.

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 tool has zero parameters and schema coverage is 100%. According to guidelines, 0 parameters earns a baseline of 4. The description adds meaning by specifying what the tool returns, which is sufficient.

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 returns the most recent weekly LLM visibility measurement for specific named models (ChatGPT, Claude, Perplexity, Gemini, Grok). It uses a specific verb ('returns') and resource ('Elephant Accountability's most recent weekly LLM visibility measurement'), distinguishing it from sibling tools like assess_fit or get_offerings.

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 provides no explicit guidance on when to use this tool versus alternatives. It does not mention when not to use it, prerequisites, or trade-offs with sibling tools. The phrase 'The receipt we publish to keep our own claims honest' hints at a use case but does not offer clear usage criteria.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden. It discloses that the tool routes to tiers and returns confirmation links, but lacks details on side effects (e.g., does it trigger emails or create tickets) or authentication requirements.

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 concise sentences that efficiently convey the tool's action, routing logic, and output. No extraneous information is present, earning every sentence 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?

Given six parameters (two required) and no output schema, the description covers overall purpose and outcome but lacks parameter explanations and usage context. It is minimally adequate but leaves gaps for agent understanding.

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%, meaning the description adds no meaning to the six parameters. It does not explain what company_name, contact_email, domain, urgency, tier_interest, or buying_context represent or how they affect behavior.

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 tool's purpose: requesting an LLM SEO audit on behalf of a buyer, with routing to different tiers and returning confirmation links. This distinguishes it from sibling tools like get_offerings or assess_fit, which are information retrieval or analysis 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 implies usage when a buyer needs an audit but does not explicitly state when to use or not use this tool, nor does it mention alternatives. There is no guidance on prerequisites or exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.