TESSA Marketing & Technology

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

With no annotations, the description carries the transparency burden and adds meaningful behavioral traits: 'Pure deterministic check, fast, no LLM call.' This informs the agent about cost, latency, and repeatability. It also clarifies the output includes recommendations, but does not mention any limitations or data source specifics.

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 concise and front-loaded with the primary purpose. It packs the mechanism, output, and key behavioral traits into a single readable sentence without redundancy. Every phrase contributes to the agent's understanding.

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 single-parameter tool with no output schema, the description is quite complete: it covers input, output structure, and key behavioral characteristics. It lacks explicit remarks on error handling or rate limits, but these are not critical for a simple deterministic URL check.

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 0%, so the description must compensate. It adds context by specifying 'website URL' rather than just a generic URL, and clarifies the input is an address to be scored. However, it provides no format guidance (e.g., protocol, domain requirements) beyond the schema's simple string type.

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 uses a specific verb ('returns a 0-100 AI Agent Readiness score') with a clear resource ('Given a website URL'). It distinguishes the tool from siblings by detailing the unique scoring output and category breakdowns, making its purpose immediately clear.

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 you need an AI readiness assessment) but does not explicitly state when to use this tool versus alternatives like get_wcag_audit or request_strategy_session. There are no exclusions or alternative suggestions, though the 'fast, no LLM call' hints at a context where speed/cost matters.

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?

The description explicitly discloses that the tool does NOT automatically grant control, logs the claim, triggers a manual email verification, and switches the verification status to 'claimed'. With no annotations provided, the description fully carries the burden and does so thoroughly.

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 three sentences, front-loaded with the primary purpose, then adding necessary behavioral details. 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?

The description explains the async process well but omits what the immediate return value or response looks like. Given there is no output schema, the agent is left without knowing whether the call returns a claim ID, status, or error. This is a clear gap for effective 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 has 0% coverage and the description does not explain any of the four parameters (firm_slug, claim_email, claimant_name, claimant_role). The description fails to compensate for the lack of parameter documentation, leaving the agent to guess the meaning and format of the inputs.

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 opens with 'Record a firm's intent to claim its directory listing,' which uses a specific verb and resource. This clearly distinguishes it from sibling tools like get_firm_profile or request_quote, which are read or quote-request operations.

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 clear context on when this tool is applicable (pre-OAuth flow) and what to expect. It notes that a representative will follow up and that a full self-service flow is planned, but it does not explicitly name an alternative tool for immediate OAuth claims.

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 the full burden. It only says 'Search' and documents parameters; it does not disclose what the search returns, whether it's read-only, how filters combine, or potential limitations. For a search tool, this lack of behavioral context (e.g., pagination, result format) is a significant gap.

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 concise and well-structured: a single opening sentence followed by a clearly formatted parameter list. Every line adds value, and the code-block layout makes the parameter documentation easy to scan. There is 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?

The description covers parameters effectively but is incomplete in other respects. It lacks any mention of return values (no output schema), how the filters interact, or search behavior beyond parameter matching. For a multi-filter search tool with no annotations, this is a moderate gap, but the parameter semantics are solid.

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 0% schema description coverage, the description fully compensates by documenting each parameter: industry (segment slug or free-text), region (case-insensitive contains), capability (keyword matching), and limit (max results). This adds substantial meaning beyond the raw schema, clarifying expected input formats and matching 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 function: 'Search the TESSA Professional Services Directory.' The verb+resource is specific and the tool name 'find_professional_services_firm' reinforces that it locates firms. However, it doesn't explicitly distinguish this from sibling tools like get_firm_profile or get_services, though those serve different purposes, so it's clear but not fully differentiated.

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: use this tool to search the directory for professional services firms. It does not provide explicit when-to-use vs alternatives, nor exclusions, but the name and context suggest it's the appropriate tool for directory searches. No guidance is given on when other tools like request_introduction or get_case_studies would be more suitable.

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 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.

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

No annotations are provided, so the description carries the burden. It discloses the return contents in detail, including directory meta (claim URL, takedown URL), which is useful. However, it does not mention error behavior (e.g., unknown slug), data freshness, authentication, or whether this is a read-only operation, though 'Returns' implies a non-mutating read.

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 concise and front-loaded with the primary action, followed by a structured list of return fields. Every line adds useful detail, and there is no redundant or filler content.

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 tool is low-complexity (one parameter, no output schema), and the description covers the main return fields. However, it lacks guidance on the slug source, error handling, and relationship to the sibling directory tools, leaving some important context implicit for an agent.

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 0% schema description coverage and a single required 'slug' parameter, the description's only hint is 'single firm in the directory', which does not explain what a slug is, its format, or how to obtain it. The description adds minimal value beyond the schema's bare 'Slug' title, failing to compensate for the low 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 the verb 'Returns' and the resource 'the full dossier for a single firm in the directory', enumerating the exact contents (description, headquarters, founded year, etc.). This distinguishes it from siblings like find_professional_services_firm (discovery) and get_services (specific subset), making the purpose unambiguous.

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 when to use this tool: when you need a comprehensive profile of a known single firm, identified by slug. It does not explicitly mention alternatives or when not to use it, but the context is clear enough for a retrieval tool, and siblings like find_professional_services_firm are not referenced as alternatives.

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?

The description discloses what the tool returns: the full catalog, per-service agent_card, a2a_endpoint, and optional filters. It does not explicitly state read-only behavior or error conditions, but with no annotations, it still provides solid behavioral context.

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 about three sentences and front-loads the core purpose, followed by output details and filters. Every sentence adds value without unnecessary restatement.

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?

Even with no output schema or annotations, the description covers the essential output fields and filter options. It could mention response format or potential error cases, but for a catalog-listing tool it provides sufficient context.

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 carries the full burden. It provides explicit allowed values for category_slug and explains service_slug as filtering to one service, adding meaningful guidance beyond the raw schema.

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 returns TESSA's full service catalog and lists included categories. It distinguishes itself from sibling tools like get_case_studies and get_firm_profile by emphasizing unique A2A card URLs and endpoints.

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 explains that the tool is for discovering services and then talking to their dedicated A2A agents, which is a clear usage context. It does not explicitly mention alternative tools or when-not-to-use, but the intended workflow is evident.

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?

Since no annotations are provided, the description carries the full burden. It sufficiently discloses the tool's read-only behavior by using 'Returns' and enumerates the contents of the returned structure. It does not cover edge cases like errors or null responses, but for a simple getter this is adequate.

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, with the key information front-loaded. Every clause adds value, detailing the return payload and the intended use case without fluff.

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 no annotations, the description is fully self-contained. It explains exactly what the tool returns and when to use it, making it complete for an agent to select and invoke 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?

The tool has zero parameters, so schema coverage is trivially 100%. The baseline for 0 params is 4; the description adds relevant context about what the returned data contains, which is more about outputs than parameters.

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 structured offering for TESSA's WCAG 2.2 AA Accessibility Audit, listing specific elements (scope, deliverables, timeline, pricing bands, paired service-type slugs, and a profile URL). It uses a specific verb (Returns) and resource, distinguishing it from sibling tools that provide other services or profile information.

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 explicitly states when to use: 'Use this when a buyer agent needs TESSA's accessibility audit offering in structured form.' This gives clear context, though it does not mention when not to use or point to alternatives.

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 behavioral disclosure. It reveals that TESSA logs the lead, notifies specific email addresses for TESSA leads, forwards a warm intro email to the firm with TESSA Cc'd for directory leads, and explicitly states no calendar booking occurs. This gives the agent a clear picture of side effects and routing without annotations.

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 three sentences, front-loaded with the main purpose, followed by routing behavior and an exclusion. Every sentence earns its place, with no redundant fluff. It is concise, well-structured, and easy to scan.

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 covers the core behavior, routing, and an alternative tool, which is solid for a request tool. However, it lacks detail on return values (no output schema) and does not clarify all parameters, leaving some gaps for an agent. Overall, it is complete enough for the task but not exhaustive.

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 for parameter clarity. It only explains target_firm_slug ('to any directory firm if you pass target_firm_slug'), leaving prospect_email, prospect_name, prospect_org, project_brief, and requested_window to be inferred from their names. This is insufficient for a 6-parameter tool with no schema descriptions, though the one explained parameter is valuable.

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 opens with a clear verb and resource: 'Request an informational introduction — to TESSA itself, or to any directory firm.' It explicitly contrasts with request_strategy_session by stating 'No calendar booking,' distinguishing it from a sibling tool. The scope (TESSA vs. directory firms via target_firm_slug) is precisely defined.

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 an explicit exclusion and alternative: 'No calendar booking — use request_strategy_session to book a meeting with TESSA.' It also clarifies the two lead types (TESSA vs. directory) and how each is handled. While it doesn't explicitly state 'use this for X,' the purpose and alternative are clear enough for proper tool selection.

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 the full burden of behavioral disclosure. It only states the basic action and a comparison but does not disclose side effects, expected outcomes, or any limitations. The parameter hint for prospect_email ('caller's email for the firm's reply') hints at behavior but is minimal.

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 concise and well-structured: a clear action sentence, a comparison sentence, and a compact parameter list with inline format hints. No wasted words; every sentence 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?

The tool has 7 parameters, no output schema, and no annotations. The description covers the core action and key parameters but lacks return value information and omits 3 optional parameter explanations. It is adequate for a simple quote request but not fully 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 description coverage is 0%, so the description must compensate. It explains 4 of 7 parameters (firm_slug, prospect_email, scope_summary, budget_band) and provides useful enum values for budget_band. However, it omits 3 optional parameters (prospect_org, prospect_name, requested_window), leaving them undocumented both in the schema and description.

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 verb+resource: 'Request a quote from a directory firm.' It also differentiates from the sibling tool request_introduction by calling it 'lighter-weight' and framing it as a budget+scope inquiry, which distinguishes its purpose.

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 explicitly names an alternative (request_introduction) and explains that this tool is 'lighter-weight' and 'explicitly framed as a budget+scope inquiry', giving clear context for when to use it. However, it does not explicitly state when not to use it or mention other sibling tools, so it stops short of full 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?

With no annotations provided, the description fully covers behavioral details: finds a slot, creates a Google Calendar event with a Meet link, emails the invite, and captures the lead if no slot is available. It also documents accepted input formats for requested_window, ensuring the agent knows the tool's actions and constraints.

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 well-structured and concise: first paragraph states purpose and behavior, second paragraph details the requested_window parameter. No filler; every sentence adds value.

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 booking tool with no output schema and rich side effects, this description is notably complete. It covers the full flow: scheduling, calendar event creation, emailing, fallback lead handling, alternative tool usage, and input format nuances. The agent has enough to invoke the tool 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 description coverage is 0%, and the description compensates for the most complex parameter (requested_window) by detailing its accepted formats. However, other parameters (prospect_email, prospect_name, prospect_org, project_brief) are not elaborated, though their names are reasonably self-explanatory. Partial compensation only.

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 function: 'Book a 30-minute strategy session with TESSA on Kevin Callen's calendar.' It uses a specific verb and resource, and it distinguishes itself from the sibling tool by stating 'directory firms use request_introduction instead.'

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?

Provides explicit guidance on when not to use the tool and points to an alternative: 'TESSA-only tool — directory firms use request_introduction instead.' It also explains fallback behavior, adding context for when this tool is appropriate.

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