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Server Quality Checklist

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Latest release: v1.0.0
Disambiguation4/5
Most tools are clearly scoped to a single resource and action, and the descriptions clarify boundaries. The main risk is among the seven analytics tools, but their differing timeframes and aggregation levels are distinct enough to avoid serious misselection.
Naming Consistency5/5
Tool names consistently follow a verb_noun snake_case pattern: list_, get_, create_, update_, delete_, set_, add_, send_, and reply_. Even mixed pluralization like list_job_applications vs get_consultation_request does not undermine the clear convention.
Tool Count2/5
At 28 tools, the server exceeds the 25-tool threshold and feels heavy even though it spans several domains. The tools are individually useful, but the overall surface is large enough that an agent may struggle to scan and choose efficiently.
Completeness4/5
Content, job postings, consultation requests, newsletters, and analytics are largely well covered with sensible lifecycle operations. Minor gaps exist, such as no single get_job_posting tool and no subscriber management beyond listing and sending, but these can be worked around.
Average 3.8/5 across 28 of 28 tools scored. Lowest: 2.7/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 1 commit in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It states the effect (updating status) but does not mention validation rules, whether existing statuses are overwritten, permission requirements, side effects, or response behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence with no wasted words. The example is front-loaded and directly useful, though the phrase 'Update the status' partially echoes the tool name.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Although this is a simple two-parameter tool, the absence of accepted status values and any behavioral or return information leaves an agent uncertain about how to invoke it correctly. The example helps but does not fully compensate for the missing annotation and schema context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description needs to compensate. It adds meaning for 'status' through the example 'move a candidate to interviewing', but 'id' is left entirely undocumented and no valid status values or format constraints are provided.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the verb (update), the resource (job application), and the specific attribute being changed (status), with a concrete example ('move a candidate to interviewing'). It is distinguishable from sibling tools like update_job_posting and update_content, though it does not explicitly differentiate itself by name.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance about when to use this tool versus alternatives such as update_job_posting or list_job_applications. The description implies its use for changing an application's status, but does not state conditions, prerequisites, or when another tool would be more appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure, but it only states the core action. It does not mention whether the posting is immediately public, whether the 'published' parameter controls visibility, what permissions are needed, or what response is returned. This is a significant gap for a creation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence with no filler or redundant wording. It efficiently communicates the primary purpose, earning every word.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with six parameters, no annotations, and no output schema, the description is too sparse. It omits return values, the effect of the 'published' flag, required-field semantics, and any operational caveats. The schema partially covers structure, but an agent lacks enough information to confidently invoke the tool in varied contexts.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is only 33%, and the tool description provides no parameter meaning beyond what the schema already has. Four of six parameters (title, location, department, published) lack descriptions in both the schema and the tool description, and the description does not compensate for this low coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Create') and a clear resource ('a new job posting on the careers page'), which distinguishes it from sibling tools like update_job_posting or delete_job_posting. It states exactly what the tool accomplishes without ambiguity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance about when to use this tool versus alternatives such as update_job_posting or create_content. The word 'new' implies it is for new postings, but no explicit context, exclusions, or alternative-routing information is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the full burden of behavioral disclosure. It does communicate that this is a read-only counting operation and that data is delivered over a trailing window, which is useful context. However, it does not disclose the response structure, whether results are returned as a time series, or how the lookback window behaves by default.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence with no filler or redundant phrasing. It directly states the key returned metrics and the time scope, and every word contributes meaning. This is an example of efficient minimal description.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a one-parameter analytics getter with no output schema, this is minimally adequate: an agent can infer it returns daily view and session counts and can invoke it with defaults. But gaps remain: the response format is unknown, days_back is not connected to the trailing window, and no distinction is made between unique sessions and unique visitors from sibling tools. Given the absence of annotations and output schema, more context would meaningfully improve selection and interpretation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema provides type, default, and bounds for days_back but no semantic description, giving 0% schema description coverage. The phrase 'trailing window' loosely maps to days_back, but the description never names the parameter, explains that it controls the lookback period, or describes the effect of changing it. This is insufficient compensation for the missing schema documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the output as daily page-view counts and unique session counts over a trailing window, which names a specific resource and metric set. The 'get' verb is present in the tool name, and the analytic focus is recognizable. It does not explicitly differentiate from siblings like get_unique_visitors_count, but the metric wording is specific enough to avoid major confusion.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance about when to use this tool versus alternatives such as get_conversion_stats, get_unique_visitors_count, or get_audience_breakdown. The phrase 'trailing window' implies a time-series use case, but the description gives no exclusions, prerequisites, or routing recommendations. An agent is left to infer selection from the name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of behavioral disclosure. It does disclose one meaningful behavior: subscribers are returned most recent first. However, it does not mention the default limit behavior, pagination, or what subscriber fields are included, so transparency is only partial.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence with no filler. Every word adds value: the resource is identified, and the ordering behavior is specified.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple one-parameter list tool, the description plus schema is largely sufficient. The resource, ordering, and optional limit are understandable. A minor gap is that the description does not clarify that the default limit of 100 means the tool may return only a subset of subscribers.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description does not mention the 'limit' parameter at all, and schema description coverage is 0%. The schema provides constraints and a default, but the description adds no semantic context to help an agent understand how the limit affects the returned list.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('List') and the resource ('email newsletter subscribers'), and adds ordering ('most recent first'). It is unambiguous about what the tool does, though it does not explicitly contrast it with sibling tools like list_newsletter_sends.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives no guidance on when to choose this tool over alternatives such as list_newsletter_sends or list_consultation_requests. It implies the use case through the resource name, but never states conditions, exclusions, or related tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must carry the behavioral burden, and it does disclose that results are recent, site-wide, and ordered most-recent-first. It does not mention return fields, pagination behavior, whether the operation is read-only, or what 'admin actions' includes beyond the examples.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single tight sentence with no redundant words. Key information—what the tool returns, scope, and ordering—is front-loaded effectively.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple with one optional parameter and no output schema, but the description still gives only a high-level idea of the response content. An agent can likely call it correctly, but it would benefit from knowing the response shape or available event types.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 0% description coverage for the single 'limit' parameter, and the description does not mention it at all. Although the parameter's schema constraints make it somewhat self-explanatory, the description fails to compensate for the low schema coverage as required.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the resource as an admin activity log and the verb as getting it, with concrete examples like content edits and publishes. It is distinct enough from sibling tools like get_content_analytics or list_content, though it does not explicitly contrast itself with them.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies the tool should be used when recent admin actions are needed, especially for content moderation or publishing activity. However, it does not state when to prefer this over related tools or mention any exclusions, leaving usage mostly to inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden of describing behavior. It does state the output behavior (breakdown by dimension with counts per value), but it does not disclose scoping, time range, read-only status, or any limits. For a simple read tool this is acceptable but not rich.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence with concrete examples and the key output detail (counts per value). There is no filler or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a zero-parameter tool, this is mostly sufficient, but because there is no output schema and no explanation of how dimensions are selected or scoped, an agent may still be uncertain about the exact response. The lack of differentiation from overlapping analytics siblings also weakens completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero input parameters, so the baseline is 4. The description adds useful meaning by giving dimension examples and clarifying the output shape, compensating for the otherwise empty schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states what the tool does: it returns a visitor breakdown by dimension with counts per value. Concrete dimension examples (device_type, browser, os, country) help distinguish it from generic analytics tools, though it does not explicitly differentiate from sibling analytics tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance on when to use this tool versus alternatives like get_unique_visitors_count, get_analytics_detail, or get_content_insights. The name and description imply a use case, but no explicit context, conditions, or exclusions are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are supplied, so the description carries the full burden of behavioral disclosure. It communicates that this is a mutating operation on an existing posting, but omits whether the update is partial or full replacement, any permissions required, reversibility, error behavior, or return value. For an unannotated mutation tool, this is a significant transparency gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single front-loaded sentence beginning with the verb and resource and ending with the id requirement. It contains no filler or redundant schema details, though the id clause could arguably be folded into parameter documentation.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a two-parameter update operation, the description plus the schema's 'Any subset' note provides enough to invoke the tool with id and a fields object. Missing context around usage alternatives and behavioral side effects is already penalized in other dimensions, and no output schema means return values are not necessarily required, but update semantics remain implicit.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds 'existing' and 'identified by id' semantics to the otherwise bare id parameter, and 'fields' maps to the fields object. Schema description coverage is only 50%, and the description does not enumerate allowed field names, though the schema partly compensates with 'Any subset of: title, department, location, type, description, published'.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description names a specific action ('Update'), a concrete resource ('job posting'), and the targeting mechanism ('identified by id'). This clearly distinguishes it from sibling create_job_posting and delete_job_posting while accurately describing the operation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Using 'existing' and 'identified by id' implies this tool is for modifying an already-created posting, not for creating or deleting one. However, it does not explicitly name create_job_posting or delete_job_posting as alternatives, so the usage guidance is implied rather than explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It adds meaningful semantics by specifying 'distinct visitors' and 'across all time,' which clarifies deduplication and aggregation scope. However, it does not explicitly state that the call is read-only or describe the exact return format.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence with no filler. It conveys the metric, uniqueness condition, and time scope in nine words, making it appropriately sized for a zero-parameter tool.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a zero-parameter count tool, the description is largely sufficient: it tells the agent what is counted and over what period. It does not explicitly declare the return type as an integer or mention that the operation is safe/read-only, but the simplicity of the tool makes it complete enough to invoke correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters and the schema description coverage is 100%, so there are no parameter semantics left undocumented. The description also reinforces this by committing to an 'all time' scope, implying no date-range or filter inputs.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the metric ('Total count of distinct visitors') and its scope ('across all time'), so an agent can understand the tool's core purpose. It lacks an explicit action verb and does not name sibling tools, though 'unique visitors count' naturally distinguishes it from daily views or audience breakdown tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance about when to use this tool instead of related siblings such as get_daily_views, get_audience_breakdown, or get_content_analytics. The description implies a use case by naming the metric, but it provides no exclusions or comparative context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must carry behavioral weight. It does disclose the append-only behavior and the 'internal' nature of the note, which is useful. However, it does not mention authorization expectations, what happens with the optional author_email, or any response/error behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
A single, front-loaded sentence with zero filler. It communicates the core action, scope, and the key non-overwriting behavior efficiently.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple, but the description leaves some gaps: no usage differentiation from reply_to_consultation, no mention of the optional author_email, and no behavioral details beyond appending. It is adequate for a basic call but not fully complete for an agent deciding between sibling tools.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 0% description coverage, so the description needs to compensate, but it only loosely maps 'note' to message and 'consultation request' to inquiry_id. The optional author_email parameter is not addressed at all, and no guidance is given on how it affects attribution.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Append') and resource ('consultation request's thread'), and explicitly clarifies that it adds an internal note without overwriting prior notes. This clearly distinguishes it from update_consultation_request and reply_to_consultation at a glance.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The wording 'internal note' implies this is for staff-side annotations rather than replying to the requester, but it does not explicitly state when to prefer this over reply_to_consultation or update_consultation_request. No explicit when-to-use or when-not-to-use guidance is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of behavioral disclosure. It explains the tool's analytical, non-mutating nature and the kind of output produced, but it does not mention data sources, time ranges, thresholds, or exact return structure. This is adequate but not fully transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, well-structured sentence. It front-loads the core purpose and then provides the categorical detail and the actionable outcome without any wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a no-parameter tool, the description is mostly complete: an agent can infer it provides an overall content performance categorization across content, not a per-item metric dump. It could be improved by clarifying whether the insight is global or scoped, but the absence of parameters plus the clear output description makes it sufficiently complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so parameter semantics are not a concern. The description does not need to explain parameters, and the no-parameter baseline of 4 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb ('get') and resource ('content insights') and clearly explains the output: content categorized as 'stellar', 'improving', or stale/underperforming, each with a suggested next action. It is distinct from raw analytics tools, though it does not explicitly name or contrast sibling tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
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 automated content performance categorization and recommended next actions are needed. However, it provides no explicit when-not-to-use guidance or alternatives among the many sibling analytics/content tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden of behavioral disclosure. 'List' and 'filter by status' convey a read-only query operation and the contact-form source is useful, but the description does not mention pagination, ordering, status match semantics, or whether any side effects occur.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two direct sentences with the primary action front-loaded and no filler. The description efficiently states what is listed and how filtering works.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple list tool with two optional parameters, this is close to adequate, but with no annotations and no output schema it leaves gaps: valid status values are only examples, limit behavior is not described, and there is no pointer to single-record or follow-up consultation tools. Naming get_consultation_request as the alternative would improve completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
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 does explain the status parameter with examples and notes that statuses are site-defined, but it says nothing about the limit parameter, its default, or its bounds. This is only partial compensation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a specific verb and resource: it lists inbound consultation/demo requests submitted via the site's contact form. This clearly distinguishes the tool from siblings like get_consultation_request (single record), list_content, and list_job_postings.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies a read/query use case and mentions filtering by status, but it never explicitly names alternatives or conditions for choosing this over get_consultation_request or reply_to_consultation. Some usage context is present, but there is no when-not-to-use guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
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 adds useful nuance by noting that status values are site-defined and not enforced by the DB, but it does not describe output shape, pagination, or ordering behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences deliver the core purpose first, then a valuable status detail. There is no fluff or redundant restating of the title.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description is adequate for a basic list operation with three optional parameters, but since there is no output schema and no annotations, it would be stronger with some indication of the returned application fields and how limit behaves.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
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 clarifies that career_id and status are filters and provides typical status values, but it does not explain limit semantics or whether filters combine with AND/OR.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
States a specific verb and resource: 'List applications submitted for job postings.' This distinguishes it from sibling list_job_postings and clearly identifies the domain and scope.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies the tool is for retrieving job applications and mentions filtering by career_id or status. However, it does not explicitly say when to prefer this over sibling tools or provide exclusions, so the usage context is largely inferred.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the burden of conveying behavior. 'Fetch' reasonably implies a read-only operation, and 'along with its internal notes thread' adds useful detail about the response contents. However, the description does not disclose error behavior, authentication needs, or whether this is purely a safe read, which would be valuable given the absence of annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single focused sentence with no filler or redundant phrasing. It front-loads the core action and resource, then adds the key extra detail about the notes thread. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple one-parameter fetch tool with no output schema, the description adequately communicates what the tool does and what the response includes — the consultation request and its internal notes thread. It does not mention possible errors or explicitly route to sibling list tools, but the operation is simple enough that these are not critical gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema only exposes a bare 'id' string with no description, and schema coverage is 0%. The description subtly clarifies that the id refers to a consultation request through 'Fetch a single consultation request', but it never explicitly defines the parameter, its format, or where to obtain the id. The meaning is inferable but not fully compensated by the description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb, 'Fetch', and identifies the exact resource, 'a single consultation request', while also specifying that the internal notes thread is included. The word 'single' naturally distinguishes it from list_consultation_requests, so an agent can tell what this tool does even without reading the schema.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies this tool is for retrieving one consultation request rather than listing multiple, but it never explicitly states when to prefer this over list_consultation_requests or any sibling tool. There is no when-to-use, when-not-to-use, or alternative guidance, so the agent must infer usage from the word 'single' and the resource name.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the behavioral burden. It discloses that the fields object must match table columns and gives column lists plus the type enum for resources. However, it doesn't mention success/error behavior, required-vs-optional fields, or what is returned after creation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The purpose is front-loaded and the column lists are dense but efficient. The only minor issue is that long inline lists are slightly harder to scan, though every piece of information earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given three content types, nested objects, no output schema, and no annotations, the description is incomplete. It omits required-field constraints, data types for most columns, and any mention of return values or validation behavior, so an agent could construct an invalid fields object.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema only defines fields as 'Column values for the new row,' so the description adds essential semantics by enumerating exact columns per table, including the nested metrics structure and the type enum for resources. This compensates for the low schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description opens with a specific verb and resource: 'Create a new blog post, case study, or resource.' This clearly distinguishes it from update/delete/set-published siblings and even excludes job postings, which have their own create tool.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives clear context for when to use it: creating new content rows. Enumerating only blog_posts, case_studies, and resources routes agents away from sibling tools like create_job_posting, though it never explicitly names update_content as the alternative for existing rows.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of explaining behavior. It clarifies that this is a read-style fetch returning all fields including body content, but it does not address potential error behavior, authentication, uniqueness of slug/id, or what happens when neither selector is provided.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, tightly written sentence that front-loads the core action and return scope. Every phrase adds useful information about what is fetched and how it is identified.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple 3-parameter tool, the description covers purpose, resource type, selectors, and return detail. But with no output schema or annotations, it could still add guidance on required inputs and how this tool relates to list_content or other content-fetching siblings.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must add parameter meaning. It maps 'blog post, case study, or resource' to the table enum and 'slug or id' to the id/slug properties. However, it does not explain that table is required, whether id/slug are mutually exclusive, or which selector takes precedence.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Fetch') and resource ('full record... for a single blog post, case study, or resource') and clearly states the lookup keys ('by slug or id'). It distinguishes itself from list-oriented siblings by emphasizing 'full record' and 'single' item.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description conveys a clear use case: retrieve the complete record of one content item when slug/id is known. It does not explicitly mention alternatives like list_content for summaries, but the 'full record' and 'single' language makes the intended context reasonably clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full behavioral burden. It discloses that conversion data is derived from tracked content views, which is meaningful, but it does not mention time scope, aggregation period, data freshness, or that this is a read-only operation. Some transparency, 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
A single, front-loaded sentence defines the metric, gives examples, and states the derivation source without filler. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description explains what is returned and gives example conversion types, but with no output schema and no annotations, the agent still lacks the complete list of possible conversion types and any time or filter semantics. Acceptable for a simple no-param tool, but not fully complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
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% and there is no parameter detail to supplement. With no input surface, the baseline 4 for zero parameters applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb and resource: get conversion stats, and precisely defines the metric as counts and rates by conversion type, with concrete examples (newsletter signup, consultation request). This clearly differentiates it from sibling analytics tools like get_unique_visitors_count and get_daily_views.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies the tool is for conversion-focused analytics, but it provides no explicit when-to-use guidance or contrasts with alternatives like get_content_analytics or get_content_insights. An agent can infer the domain, but not the decision boundary between this and other analytics tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden and does convey the core behavior: flipping the published flag and optionally scheduling a future publish. It also discloses the table restriction for publish_at. It does not mention response shape, permissions, or side effects like content becoming immediately public, but for a simple state toggle this is a moderate disclosure.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two concise sentences with no filler. The primary action is front-loaded, and the important publish_at constraint is included compactly in the second sentence.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers the main invocation semantics for a relatively simple tool, and the input schema supplies the required parameters. However, there is no output schema and no mention of what the tool returns or whether scheduled publishing requires published to be true, leaving some behavior to inference.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is only 25%, but the description adds meaning by mapping 'table' to blog posts, case studies, and resources, clarifying that 'published' is a flag to toggle, and explaining that publish_at is for scheduled publishing on blog_posts and case_studies only. The slug parameter is left to inference, but its role is fairly obvious.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb and resource: 'Toggle the published flag on a blog post, case study, or resource.' This clearly distinguishes it from generic tools like update_content and destructive tools like delete_content. The title reinforces the publish/unpublish purpose.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives clear context for when to use the tool: whenever the published state of content needs to change or a scheduled publish is needed. However, it does not explicitly compare against update_content or state when not to use it, leaving the agent to infer the specialization.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
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 reveal useful behavior: per-content grouping, specific metrics included, and descending sort by view count. However, it does not mention the time period covered, pagination behavior, response format, or whether results are limited in any way.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences with no filler. The key metrics are front-loaded, scope is clearly stated, and the sort behavior is specified in the second sentence. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a zero-parameter analytics read tool with no output schema, the description covers the essential return values and sort order, and identifies the content categories included. It is missing a temporal scope clarification (all-time vs. recent window), but overall it gives an agent enough to call and interpret the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters and the schema description coverage is 100%, so no parameter documentation is needed. The description correctly focuses on output semantics rather than input semantics, which is exactly what an agent needs for a no-argument tool.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description names a specific resource ('per-content-item') and the exact metrics returned: view counts, average time on page, average scroll depth, and last-viewed timestamp. It also states scope (blog posts, case studies, resources) and sort order, which distinguishes it well from sibling analytics tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The tool's purpose is clear enough that an agent can infer when to use it for per-content engagement metrics. However, the description gives no explicit guidance about when not to use it or which sibling tools (e.g., get_daily_views, get_content_insights, get_analytics_detail) might be better suited for other analytics needs.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the behavioral burden. It adds the key default behavior that both published and unpublished items are returned unless filtered, which is not an obvious consequence of the schema alone. It does not mention pagination or other response traits, but for a simple list tool the stated behavior is sufficient.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
A single, front-loaded sentence covers purpose and key behavioral nuance without filler. Every clause earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a straightforward listing tool with a small schema, the description plus schema provides enough to call it correctly: table is required, limit has defaults and bounds, and published_only's effect is stated. The lack of an output schema means return-structure details are not provided, but this is not a critical gap for correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema covers only table (33% coverage). The description helps by enumerating the content categories matching the table enum and by indicating the filtering effect for published_only, but it does not explain the limit parameter or explicitly map 'unless filtered' to published_only.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb ('List') with a concrete resource scope ('blog posts, case studies, or resources'), which clearly differentiates this tool from sibling listing tools like list_job_postings and list_consultation_requests.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Usage context is implied by naming the three content types, but the description never explicitly says when to choose this tool over sibling list tools or any exclusions. No alternative tools are named.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
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 explicitly warns that the tool 'actually dispatches real emails' and advises user confirmation before calling. This effectively communicates the irreversible, side-effectful nature of the action, though it omits details such as return behavior or error conditions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely compact: two sentences, front-loaded with the core action and immediately followed by a critical safety warning. Every sentence earns its place with no filler or repetition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a 2-parameter tool with no output schema and no annotations, the description provides the essential safety context and invocation requirements. It could be more complete by describing what happens after sending or how to confirm success, but the core information needed to select and invoke the tool safely is present.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description mentions 'subject and HTML content,' which maps to the two required parameters, but adds no format, length, or content constraints beyond the schema. Since schema coverage is only 50% and the 'subject' property has no schema description, the description only partially compensates for the missing parameter context.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('Send'), the resource ('a newsletter campaign to all subscribers'), and the mechanism (site send-newsletter/Resend). It is easy to distinguish from sibling read-only tools like list_newsletter_sends or list_newsletter_subscribers.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when to use it: whenever the user wants to broadcast a newsletter to all subscribers. It includes an important usage precondition — confirm subject and HTML with the user before calling — but it does not explicitly contrast this with any alternative tool or state when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
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 disclose a genuinely important trait — that admin_notes is overwritten rather than appended — which prevents the agent from misusing the tool destructively. However, it stops short of stating partial-update semantics (e.g., whether omitting admin_notes clears it), permission requirements, or what the response looks like.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences with zero waste: the first front-loads the core operation and scope, the second delivers the routing guidance. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple 3-parameter tool with no output schema, the description covers the core operation and the key sibling distinction. But with 0% schema coverage and no annotations, the missing valid status values and unspecified partial-update behavior leave real gaps an agent must resolve elsewhere.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
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 names the two updatable fields (status, admin_notes), which maps to the schema properties, but it gives no guidance on valid status values, id format, or constraints on admin_notes. An agent still has to guess what strings status accepts, leaving a significant gap at 0% coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb ('Update'), a precise resource ('a consultation request'), and the exact scope ('status and/or admin_notes'). It also distinguishes itself from the sibling add_inquiry_note, so an agent can tell it apart without opening the schema.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
It explicitly names the alternative (add_inquiry_note) and the condition that selects it: append a timestamped note rather than overwrite admin_notes. This is ideal routing guidance and leaves nothing to inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses that the operation is paginated, returns raw events rather than aggregated summaries, and lists the detail fields. It does not discuss auth or rate limits, but for a straightforward read-only retrieval tool the behavioral picture is reasonably complete.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two tight sentences carry the key information up front: what is returned, the detail level, and the intended use. No filler or repetition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a 2-parameter tool without an output schema, the description covers the return fields and purpose well. It omits the default time/date window (no date filter exists in the schema) and response ordering, which an agent may need to correctly interpret paginated raw analytics data. Still, it is enough to select and invoke the tool for basic drill-down.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description should compensate. The word "Paginated" usefully contextualizes limit/offset, but the description does not explain exact pagination semantics (e.g., whether offset is zero-based or page-based) or the data window being paged. The schema's defaults and bounds provide the rest.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a specific verb and resource: "Paginated raw page-view events" and enumerates the returned fields (session, device, browser, os, etc.). It clearly differentiates itself from summary analytics siblings by emphasizing raw event-level detail.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
It explicitly says "Use for drill-down investigation" and "prefer the summary tools for regular reporting," giving clear when-to-use and when-not-to-use guidance. However, it refers to "summary tools" by category rather than naming the specific sibling tools, leaving a little room for ambiguity.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Although no annotations are provided, the description signals a read-only, historical retrieval operation through the words 'List past ... that were sent' and enumerates the returned data. It does not mention ordering or pagination beyond the schema's limit parameter, but the core behavioral context is clear.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence that states the resource, scope, and output fields without wasting words. Every phrase earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simple one-parameter schema and no output schema, the description sufficiently explains what the tool returns and what data users can expect. It leaves out ordering/recency details, but the schema covers the limit parameter and the core purpose is fully described.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description does not mention the optional 'limit' parameter. The input schema provides default/min/max constraints, but the description adds no meaning to help an agent understand pagination or how limit affects results.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb and resource: it says 'List past newsletter campaigns that were sent' and names the returned fields (subject, sent/failed counts, timestamps). This clearly distinguishes it from siblings like send_newsletter or list_newsletter_subscribers.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description makes the context clear: this tool is for retrieving historical sent campaign information, not for sending newsletters or listing subscribers. It does not explicitly name alternatives or exclusions, but the usage context is unambiguous.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations, so the description carries the burden. It discloses partial-update semantics and row addressing via slug or id, but omits what happens when no row matches, whether both id and slug are accepted/rejected, and no response shape info.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences, front-loaded with purpose and resource types; the second adds operational instructions with no filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a mutation with no annotations and no output schema, the description handles the basics but leaves the id/slug requirement unenforced and unclear, and gives no expectation of return value. The fields object is intentionally open-ended, which is acceptable, but the identifier ambiguity is a real gap.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With only 25% schema coverage, the description compensates meaningfully: table maps to the three content types, slug/id are identified as alternative row keys, and fields is explained as a partial-change object. It doesn't state whether id/slug is required (schema marks neither), leaving some ambiguity.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
States a specific verb ('update') and resource scope ('blog post, case study, or resource'), and clarifies it targets existing rows, distinguishing it from create_content and delete_content. The identifier options (slug/id) add operational precision.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Clear context: use when modifying fields on existing content rows. The 'existing' wording and 'pass only the fields you want to change' imply not for creation or full replacement, but it never names alternative tools or exclusions (e.g., set_content_published for publishing-only changes).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden of behavioral disclosure. 'List' clearly indicates a read operation, and the filter behavior is explicit, but the description does not address pagination, ordering, return format, or permission requirements. It is adequate but not richly transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
A single, front-loaded sentence states the action, the resource, and the one parameter nuance. There is no wasted wording, and the optional filter is placed exactly where it is useful.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a low-complexity tool with one optional boolean parameter, no required parameters, and no output schema, the description is largely complete. It omits non-essential details like return shape or pagination, which are not necessary for an agent to select and invoke the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, but the description compensates by explaining the 'published-only' filter, which maps directly to the published_only boolean parameter. It makes the parameter's optionality clear, and the schema's default of false is consistent with 'optionally filtered.'
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific action ('List') and a specific resource ('career/job postings'), which clearly distinguishes it from sibling tools like create_job_posting, update_job_posting, and list_job_applications. The optional published-only filter adds further precision to what the tool returns.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives clear context for use: call this tool when you need job postings and optionally want to restrict results to published ones. It does not explicitly name alternative tools or exclusions, but the intended use case is obvious for a simple read-only list.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
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 clearly states two side effects: a real email is sent and the request is marked as replied, plus a caution to confirm with the user. It could go further by mentioning irreversibility or limitations, but the key consequences are explicitly disclosed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences with no wasted words. The first sentence states the action and mechanism, the second discloses side effects, and the third gives the critical confirmation requirement. Essential information is front-loaded and every sentence contributes value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a side-effectful email-sending tool with no output schema and five required parameters, the description covers the core behavior, side effects, and user confirmation step. It does not mention error cases, rate limits, or permission requirements, but these are not expected given the context and the description provides enough for an agent to safely invoke the tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is only 20% (only body has a description), so the tool description should compensate. It provides context that to_email/to_name refer to the requester and that consultation_id identifies the submitted request, but it does not explicitly describe each parameter's format, constraints, or expected combination. The parameter names are mostly self-explanatory, and the description supplies enough context to infer usage, earning a midpoint score.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb 'Send an email reply' with a clear resource 'someone who submitted a consultation/demo request', and explicitly distinguishes this from other consultation-related operations by noting it uses the site's reply-consultation function and sends a real email. It clearly differs from siblings like get_consultation_request or update_consultation_request.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description states the operation sends a real email and marks the request as replied, which implies when it is appropriate. It adds a direct usage instruction: 'Confirm the message with the user before calling.' However, it does not explicitly tell the agent when to use this instead of update_consultation_request or other alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
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, and it does clearly signal that deletion is permanent and that history is lost. It does not, however, disclose whether related resources such as job applications are also affected, which would be useful for a destructive operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two concise sentences with no filler. The core action and permanence warning are front-loaded, and the alternative is mentioned in a compact, actionable way.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
This is a simple single-parameter tool, and the description covers its main usage and the key alternative. However, because there is no output schema and no annotations, a bit more detail about post-deletion effects or related data would make it fully complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description never explains the meaning of the id parameter beyond what the schema's type: string already states. An agent has to infer that id identifies the job posting to delete, with no guidance on format or where to obtain it.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Permanently delete a job posting') with a precise verb and resource. It also distinguishes the tool from update_job_posting by framing deletion as the permanent counterpart to unpublishing, so an agent can easily tell which operation to use.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives explicit when-to-use guidance: prefer update_job_posting with published: false for closing applications while preserving history. This directly names the alternative tool and the condition that should redirect the agent away from deletion, leaving no ambiguity.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full behavioral disclosure burden. It clearly warns that deletion is permanent and cannot be undone, and it requires user confirmation before invoking. This is substantial and highly relevant for a destructive operation, though it does not mention permissions or failure behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences with no filler. The destructive action is front-loaded, and the irreversibility warning earns its place. Every word contributes to correct invocation and safe usage.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple two-parameter deletion tool with no output schema, the description is complete enough. It identifies what is deleted, how it is identified, warns about irreversibility, and instructs user confirmation. Nothing needed for safe invocation is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
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 does by explaining that the slug identifies the target and by naming the three content types that correspond to the table enum values. It adds meaningful human-readable semantics beyond the bare schema property definitions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb ('Permanently delete'), the resource types ('blog post, case study, or resource'), and the lookup method ('by slug'). This clearly distinguishes it from content retrieval, update, or publish operations among the sibling tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives clear usage context by scoping deletion to three content types and explicitly instructs the agent to confirm with the user before calling. It does not name alternative tools or state explicit when-not-to-use conditions, but the scope is clear enough to avoid obvious misuse.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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