mediora-mcp

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 discloses that the pipeline is asynchronous (returns pending ID for polling) and lists constraints. However, it does not mention error handling, authentication flow beyond bearer_token, rate limits, or whether the analysis is destructive. The description provides some behavioral traits but misses several important aspects.

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 four sentences, front-loaded with the main action and result, followed by constraints. Every sentence is informative and necessary. No redundant or vague wording.

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's complexity (async analysis with constraints, polling), the description covers the key aspects: what it does, URL requirements, and output. It lacks guidance on how to poll the returned ID or what to do on failure, but the sibling tools can fill that gap. Overall, it is fairly complete for a tool returning an ID.

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 100%, so the baseline is 3. The description reinforces the file_url parameter with additional constraints (public IP, no redirects, file type/size) and clarifies the bearer_token's origin (from mediora__whoami). It adds value but does not significantly expand meaning beyond the schema for the language parameter.

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 uploads a lab-report PDF via URL and triggers an analysis pipeline, returning a pending test ID. It specifies the resource (lab-report PDF), action (upload/trigger), and output (pending test ID). This clearly distinguishes it from sibling tools which are explanation or listing 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 provides explicit requirements: HTTPS URL, public IP, file type/size limits, no redirects. It implies usage scenarios (when you have a compliant PDF/JPEG/PNG) but does not explicitly state when not to use it or mention alternatives like other tools. However, the constraints serve as strong usage 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 carries the transparency burden. It explicitly discloses what the returned explainer contains and the lookup method (by slug), making the read-only, informational nature clear. It does not discuss errors or auth, but these are not critical for a simple retrieval tool.

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 a single front-loaded sentence that states the action and resource, followed by a compact list of included content. There is no filler 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?

There is no output schema, so the description's content list is necessary and sufficient for an agent to understand the return value. It covers the key aspects of the explainer (definition, markers, symptoms, when to seek care), omitting only minor details like localization behavior, which is already in the schema.

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 description coverage is 100%, with both parameters fully documented (slug with examples and discovery tools, lang with enum and default). The description adds no additional parameter-specific semantics beyond 'by slug,' which is sufficient given the complete 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 uses a specific verb ('Return') and clearly identifies the resource ('full explainer for a clinical condition by slug'). It enumerates the content included, which distinguishes it from sibling tools like explain_marker or explain_symptom.

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 clearly signals that this tool is for condition-level explanations (not markers, panels, or symptoms), giving clear context. It does not explicitly name alternatives or exclusions, but the scope is unambiguous, and the schema adds guidance to discover slugs via list tools.

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 clearly indicates a read-only retrieval ('Return') and describes the content. But it doesn't mention behavior for invalid slugs, language handling, or any side effects; still adequate for a simple lookup.

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 the main action, and efficiently packs the content list 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 a 2-param tool with no output schema, the description sufficiently explains the returned data (measurements, interpretations, actionability, conditions). It's complete for the 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?

Schema covers 100% of parameters with descriptions, so baseline 3. The description adds that slug is for a blood-test marker and how to discover it, but doesn't add new meaning beyond 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?

Clearly states the tool returns a full explainer for a single marker by slug, listing included content. Distinguishes from sibling explain_* tools by focusing on marker and mentioning blood-test context.

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 context that this is for a single marker and suggests using list_markers to discover slugs. However, it doesn't explicitly contrast with lookup_marker or other alternatives, relying on implied differentiation.

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 clearly indicates a read-only retrieval operation returning markers and aliases. However, it lacks details on potential errors, rate limits, or edge cases like invalid slugs. Still, the core behavior is well-described.

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, front-loaded with the primary action, no redundant words. 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 simple tool with 2 parameters and no output schema, the description fully captures what the tool returns and how to use it. It connects to related tools (list_panels, explain_marker) to provide a complete usage 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 coverage is 100% with both parameters described. The description adds meaning by providing example panel slugs and noting that lang defaults to 'en', and instructs the agent on how to discover valid slugs using a sibling tool.

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 states 'Return the markers that make up a lab panel by slug' and specifies exact output fields (slug, name, summary, aliases). It clearly distinguishes from sibling mediora__explain_marker by saying 'Follow each marker into mediora__explain_marker for full detail.'

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?

Explicitly answers 'what's included in a CBC / lipid panel / CMP' with examples. Provides guidance to use mediora__list_panels to discover slugs and recommends chaining with mediora__explain_marker for deeper analysis.

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 behavioral disclosure. It does well by enumerating the return content (meaning, causes, lab work-up, urgent evaluation) and explaining the graph-following behavior via slug arrays. It does not mention error handling, permissions, or other operational details, but for a read-only explainer the disclosed information is substantive.

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 only two sentences but packs in the tool's purpose, output sections, and a hint about how to traverse to related tools. Every sentence earns its place, with the most critical information 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?

There is no output schema or annotations, so the description must explain what the tool returns; it does this clearly by listing return components and the graph-following arrays. It does not explicitly cover the lang parameter's effect or error cases, but given the tool's relatively simple scope and the strong description, it is adequately complete for an agent to use it 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 baseline is 3. The description adds no extra parameter-level detail beyond the schema: slug and lang are already fully described in the input schema. The phrase 'by slug' reinforces the slug requirement but does not enhance the schema's existing 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 uses a specific verb ('Return') and identifies the exact resource ('full triage explainer for a single symptom by slug'). It clearly differentiates from siblings like mediora__explain_marker and mediora__explain_condition by focusing on 'single symptom' and even mentions how its slug arrays link to those siblings.

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 clearly conveys when to use the tool: to get a symptom's triage explainer by slug. It also implicitly guides the upstream LLM to follow related marker/condition slug arrays into sibling tools, which helps with context. However, it does not explicitly state when not to use it or name alternative tools, so it stops short of a 5.

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 full burden. It discloses pagination (default 10 per page), authentication requirements (patient-scope JWT), and that the tool proxies an external API with token forwarding. This is comprehensive 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?

Two sentences, no filler. The most critical info (purpose, authentication, pagination) is front-loaded. Every sentence is earned.

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 lists the returned fields. It covers authentication, pagination, proxying, and required token type. This is fully adequate for a tool with 3 parameters and no output schema.

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 description adds value beyond schema by specifying that bearer_token must be a patient-scope JWT and providing pagination context. It could further explain the page parameter's 1-based nature (already in schema) or the token's source.

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 ('Return') and resource ('patient's past medical-test rows'), lists the returned fields, and distinguishes from sibling tools like mediora__get_test_details by implying this is a list/history endpoint.

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 states the bearer token must be a Mediora patient-scope JWT and references mediora__whoami for obtaining it. It mentions pagination defaults. However, it does not explicitly guide when to use this tool vs alternatives like mediora__get_test_details.

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 function (returns markers + analysis), authentication method (Bearer token forwarded), and proxied endpoint. It implies read-only behavior but does not explicitly state idempotency or error conditions (e.g., unauthorized access, missing test ID). Adequate but not exhaustive.

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 long with zero wasted words. It front-loads the core purpose and immediately provides usage context and authentication details. 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?

For a simple tool with two parameters and no output schema, the description adequately explains what is returned (markers + AI analysis) and the prerequisite call (get_patient_history). It lacks information on response format or potential error responses, but given the simplicity, it is largely 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%, so baseline is 3. The description adds value by clarifying that `test_id` is 'returned by mediora__get_patient_history as `id`' and that `bearer_token` is a 'Mediora patient JWT (see mediora__whoami)'. This contextual guidance goes beyond the schema's minimal descriptions.

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 'persisted markers + AI analysis' for a specific medical test, listing the exact components (Summary, KeyFindings, Recommendations, DetailedExplanation). It identifies the resource ('medical-test') and scope ('authenticated patient owns'), distinguishing it from sibling tools like 'explain_marker' or 'longitudinal_trend'.

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 instructs when to use: 'Use after mediora__get_patient_history surfaces a test id the user wants to discuss.' It also specifies authentication requirements (patient-scope JWT). However, it does not explicitly state when not to use or provide 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?

Describes return fields but does not disclose whether the list is complete, paginated, or any additional behavioral aspects like read-only. Given no annotations, more detail could improve transparency.

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, no extraneous information. Each sentence adds value: first states purpose and output, second provides usage guidance.

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?

Adequately describes purpose, output, and alternative for a simple list tool with one optional parameter. Return values are stated, so lack of output schema is compensated.

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 the only parameter (lang) with enum and description, so baseline is 3. Description does not add further context about the parameter's behavior or defaults beyond the 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?

Clearly states it lists clinical conditions from the Mediora catalog and specifies return fields (slug, name, ICD-10). Differentiates from sibling 'mediora__explain_condition' by noting that tool provides full explainer.

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?

Explicitly directs to mediora__explain_condition for full details, indicating when not to use this tool. However, does not provide more exhaustive when-to-use or when-not-to-use 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?

With no annotations provided, the description carries the burden of behavioral disclosure. It states the return format ('slug + short name per entry'), which is a meaningful behavioral trait. However, it does not mention pagination, ordering, or language-related behavior, though the schema covers the lang parameter. The disclosure is solid but could be slightly richer.

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 long, front-loaded with the primary action in the first sentence. The second sentence provides output format and usage guidance. Every word earns its place, with no fluff or repetition of schema details.

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's simplicity (one optional parameter, no output schema), the description is complete: it states what the tool does, what it returns, and how to get more detail. It leverages sibling-tool context and does not need to explain return values since none are in schema. The description fully equips an agent to decide when and how to invoke it.

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 only parameter, 'lang', is fully described in the schema (enum with values and description). The description adds no additional parameter-specific information, so per the high schema coverage (>80%), the baseline score of 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 action: 'List every blood-test marker in the Mediora catalog.' It specifies the exact resource (blood-test markers) and scope (every), and distinguishes itself from sibling tools like list_conditions, list_panels, and list_symptoms. The additional detail 'Returns slug + short name per entry' further clarifies the tool's output.

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 provides an alternative: 'Use mediora__explain_marker for the full explainer.' This tells the agent when to choose this list tool over a detailed explanation tool, establishing a clear use case. It also implies this tool is for getting a quick catalog overview.

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, but the description is transparent about the tool's read-only nature and return format. It does not disclose any potential side effects or auth requirements, but for a simple list tool, 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 sentences, front-loaded with the action and examples. Every word adds value with no 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 zero-parameter listing tool, the description is sufficient. It explains the return structure and points to the sibling tool for more detail. Minor missing: no mention of pagination or ordering, but likely not needed.

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 are zero parameters, and the schema coverage is 100%. The description compensates by listing example panels and clarifying the return structure, adding value beyond the empty 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 specifies the action (list), the resource (lab panels in Mediora catalog), lists examples, and states what it returns (slug, name, marker count). It clearly distinguishes from sibling tool mediora__explain_panel.

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 tells when to use this tool (list panels) and directs to mediora__explain_panel for panel marker details, providing clear guidance on 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?

Description discloses that it returns a list of all symptoms with slug and short name. No annotations are provided, so description carries the burden—it does well but could mention if the list is exhaustive or any default ordering.

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 sentence states purpose and example, second sentence directs to sibling tool. No wasted words, front-loaded with key 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?

Given the simplicity (one optional parameter, no output schema), the description fully covers what the tool does and what it returns. No gaps.

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% for the single lang parameter, with a clear description in the schema. The tool description does not add additional parameter guidance, but the schema already sufficiently documents it. 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?

Description clearly states it lists all patient-side symptoms with examples (fatigue, joint pain, headache) and specifies the return format (slug + short name). It also explicitly distinguishes itself from the sibling tool mediora__explain_symptom.

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 explicitly states when to use this tool (to list symptoms) and when to use an alternative (mediora__explain_symptom for detailed explainer with lab work-up). No ambiguity.

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 explicitly describes the return structure (marker slug, kind, severity, trend per year, sample count, date range, headline + detail) and the auth mechanism (bearer token proxy). This fully discloses the tool's behavior without ambiguity.

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 long. The first sentence states the core functionality with examples, and the second adds detail on output and proxy. No unnecessary words; 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?

Given the tool's simplicity (one parameter, no output schema, no nested objects), the description is fully complete. It explains the purpose, the output structure, and the authentication method. No gaps remain for an agent to misuse the 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?

The single parameter 'bearer_token' is fully described in the schema. The description adds significant value by explaining how to obtain the token (DevTools or pass-through from desktop client) and why it is needed. This goes beyond the schema's minimal 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 specifies the verb 'Return' and the resource 'per-marker trajectory analyser output for the authenticated patient'. It provides concrete examples (HbA1c rising, eGFR falling) and details the output fields, distinguishing it from sibling tools that list or explain markers without trend analysis.

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 proxies a specific API endpoint and forwards auth tokens, giving context on how it works. It does not explicitly state when to use versus siblings, but the purpose is clear enough for an agent to infer it is for longitudinal trends. No exclusion criteria 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 the full burden. It mentions 'fuzzy-search' and cross-language matching, which adds behavioral context beyond the schema. However, it does not disclose details like whether multiple results can be returned, pagination, or result scoring. A score of 3 reflects that it provides some but not comprehensive behavioral insight.

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 with no extraneous information. It front-loads the operation ('Fuzzy-search markers by alias across EN/RU/HE') and provides a practical usage example. 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?

Given the simplicity of the tool (2 parameters, no output schema), the description adequately covers the overall purpose and usage context. It could be improved by briefly mentioning the output format (e.g., 'returns the canonical slug'), but it is sufficiently complete for an agent to understand the tool's role.

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%, both parameters (query, lang) are described with examples and enum values. The description adds context about fuzzy-search and canonical slugs, but does not significantly enhance the parameter meaning beyond the schema. Baseline score of 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 performs fuzzy-search by alias across three languages and returns the canonical Mediora slug. It uses a specific verb ('fuzzy-search') and resource ('markers by alias'), and distinguishes it from siblings like list_markers or explain_marker.

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 says 'Useful when the upstream LLM has the lab's local name... and needs to find the canonical Mediora slug.' This gives clear guidance on when to use the tool. However, it does not mention when not to use it or provide explicit alternatives, which would improve clarity.

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 fully describes the tool's behavior: it validates a token and returns id+email. It also explains token source. Missing details like error handling or what happens with invalid tokens, but overall transparent enough for an auth validation tool.

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: the first states the core function and output, the second provides usage guidance and token source. Extremely concise with no wasted words, front-loading the most important 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?

Given the tool's simplicity (single parameter, no output schema), the description covers everything needed: purpose, token retrieval, and when to use. It is fully complete for an authentication validation 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?

The schema already gives 100% coverage with a description for the bearer_token parameter. The tool description adds significant value by explaining exactly how to obtain the token (browser DevTools or desktop client), which is crucial context beyond the 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 specifies the tool validates a Mediora.AI patient Bearer token and returns the patient id and email. It uses a specific verb+resource pair, distinguishing it from all sibling tools that are for analysis, explanation, or lookup.

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?

Explicitly states to use this tool first to confirm the token before calling other auth-gated tools, providing clear timing and purpose. It also explains how to obtain the token, though it does not explicitly discuss when not to use it, but the context makes it obvious.

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