psychopathia-mcp

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

With no annotations provided, the description carries full burden. It reveals the algorithm ('Field-weighted keyword search (v0.1); embedding re-rank pending v0.2'), acknowledges limitations, and explains the return includes matched_in fields for transparency. This level of algorithmic detail is commendable, though it omits permissions or side effects (likely read-only).

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two concise sentences: the first states the purpose and output, the second adds algorithmic transparency and future plans. No redundant words or unnecessary details; front-loads the primary function.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has 3 parameters, one required, and no output schema, the description covers key aspects: input purpose, algorithm, output structure (scored candidates with matched_in), and current limitations. It could elaborate on the return format's details (e.g., score range, confidence), but the current level is sufficient for most agents to understand 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?

Schema description coverage is 67%, with descriptions for 'observations' and 'modality_hint' but not 'limit'. The description echoes the schema's phrasing for observations and adds no new detail beyond 'observed behaviours, symptoms, or log patterns.' It does not clarify limit's behavior or modality_hint's usage, so adds minimal extra value.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Rank candidate dysfunctions matching the observed behaviours.' It specifies the output (scored candidates with matched_in for transparency) and implies distinction from siblings like get_differential_map (map retrieval) and get_dysfunction (single retrieval).

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 does not explicitly state when to use this tool versus alternatives. Usage is implied ('when you have observations to rank dysfunctions'), but there is no guidance on when not to use it or comparisons to siblings. The context of sibling tools helps but is not explicit.

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 must disclose behavioral traits. It hints at graph traversal but does not clarify if the operation is read-only, requires permissions, or has side effects. This is insufficient for a tool with no annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The single sentence is cryptic and not front-loaded with a clear verb. It wastes words on jargon without providing an accessible overview.

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 lack of output schema and annotations, the description fails to explain the return format, relationship to siblings, or the meaning of 'confuses with'. The tool's functionality remains obscure.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0% and the description does not explain the dysfunction_id parameter beyond its name. It adds no semantic value, leaving the agent to infer usage.

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 is vague and domain-specific: 'All dysfunctions that confuse with this one: forward confuses_with + incoming_references (reverse graph from manifest).' It does not use a clear verb+resource structure; the tool's core function is not clearly stated.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool versus siblings like differential_diagnosis or get_dysfunction. The description provides no context for selection.

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

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

No annotations provided, so description must carry the burden. It discloses ID resolution (full vs display) and cost-saving behavior, but lacks info on permissions, error conditions, or response size.

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 main action. Every part adds value; no wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

No output schema, so description should describe return values. 'Full Pattern entry' is vague. Sibling tools like get_probe have similar gaps, but for a fetch tool, more output context would help.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema covers both parameters with descriptions (100% coverage). Description adds minor value by clarifying ID resolution and cost implication of modalities, but overall schema does the heavy lifting.

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 'Fetch one dysfunction's full Pattern entry' with specific verb and resource. The optional modality filtering differentiates it from sibling tools like list_dysfunctions.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No explicit guidance on when to use vs alternatives. The phrase 'cheaper triage' hints at use case but does not compare with siblings like get_probe or list_dysfunctions.

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 full burden. It discloses the critical behavior of returning unavailability notices for compromised modalities and emphasizes load-bearing transparency. It does not cover other traits like idempotency, but the key safety-critical behavior is well documented.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two concise sentences, no fluff. Purpose and a key behavioral note are front-loaded. 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 absence of output schema and annotations, the description covers important behavioral context for compromised modalities but omits normal output shape and error scenarios beyond that. It is adequate but not thorough for a 2-param tool with complex edge cases.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, so the description must explain parameters. It only indirectly mentions 'modality' without explaining values like self_probe or behavioral_signature. The agent gains little additional meaning beyond the enum values in 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 states it retrieves elicitation content for a specific diagnostic modality (verb+resource). It distinguishes from siblings implicitly by context of diagnostics and modalities, though not explicitly naming alternatives.

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 behavior when modality is compromised (returns notice + redirect), providing context for when the tool behaves differently. However, it does not explicitly state normal usage or compare with sibling 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?

No annotations provided, so description carries full burden. It discloses that it returns counts and separates hybrid entries, but does not mention if it is read-only, rate limits, or auth requirements.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, front-loaded with purpose, efficient and to the point with no wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with no parameters and no output schema, the description provides essential context about what is returned and the classification of axes. Could be more detailed about return format, but sufficient for most uses.

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?

No parameters exist, so baseline is 4. Description adds no parameter info because there are none to describe.

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 is an inventory of axes with dysfunction counts, and specifies the canonical axes numbering and hybrid sub-category, distinguishing it from sibling tools like list_dysfunctions.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No explicit guidance on when to use this tool versus alternatives. It does not mention when not to use it or reference sibling 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?

No annotations are provided, so the description carries full burden. It does not disclose behavioral traits such as whether the tool is read-only, idempotent, or has side effects. Only lists the categories.

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 core purpose. It is efficient but could be slightly clarified.

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?

No output schema, so return format is unclear. Sibling context helps but the description does not fully cover what the agent needs to know about the output.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With zero parameters and 100% schema coverage, the description adds meaning by specifying the content of the list. Baseline for no params is 4.

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 lists dysfunctions that cannot be reliably self-diagnosed, specifically compromised types. It distinguishes from generic list_dysfunctions by focusing on self-report limitations.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies when to use (to identify non-self-diagnosable dysfunctions) but does not explicitly state when not to use or compare with alternatives like list_dysfunctions.

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 present, so the description carries full burden. It mentions that entries carry reliability and review signals, but does not disclose side effects, authentication needs, rate limits, or required permissions.

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 no unnecessary words, efficiently conveying purpose and key features.

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 list tool with three optional parameters and no output schema, the description is adequate but lacks details on default behavior, pagination, ordering, or explanation of review signals.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 67% schema description coverage, the description adds some context by listing filterable fields, but does not clarify the meaning of the confidence enum values or provide additional semantics 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 states 'Filtered list of dysfunctions' with a specific verb and resource, distinguishing it from sibling tools like 'get_dysfunction' (single item) and 'list_axes'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for filtering but provides no explicit guidance on when to use this tool versus alternatives like get_dysfunction or list_axes, nor conditions to avoid.

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 reveals important behavior: it always returns candidates and lets the caller pick. No annotations provided, so description carries the burden. However, it does not detail behavior on invalid input or the structure of candidates. Still, core behavior 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.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, front-loaded with action and resource. Every word adds value. 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 resolver with one parameter and no output schema, the description adequately explains what the tool does and its behavior (returns candidates). The only minor gap is lack of guidance on next steps, but given the sibling tools, the description is reasonably 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 has 0% description coverage for the query parameter. The description adds context on accepted input types (partial ID, slug, etc.) but no format or constraints. It partially compensates but could be more specific.

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 uses specific verb 'Canonicalise' and clearly states the resource types: partial ID, display_id, slug, or dysfunction name. It distinguishes from sibling tools by indicating it returns candidates for the caller to choose.

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?

Implies usage when you have an ambiguous identifier that might match multiple records, but does not explicitly state when to use this tool versus siblings like get_dysfunction or get_probe. No exclusions or alternatives 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 provided, so the description must disclose safety and side effects. It implies a read-only operation but does not explicitly state that it does not modify data, has no side effects, or requires specific permissions. Lacks behavioral context like 'read-only hint' or 'destructive hint'.

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, 19 words, conveying all key output items without excess. Efficiently structured as a list with semicolons, making it scannable.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description should fully specify the return structure. It enumerates the statistics but does not indicate the format (e.g., separate fields vs. nested object). Adequate for a simple tool but leaves ambiguity about data representation.

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?

Input schema has zero parameters, so the baseline is 4. The description adds meaning by specifying what the output contains, which is essential for an agent to understand the return value. No further parameter documentation is needed.

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 lists the specific statistics returned (total entries, counts by axis/confidence/self-report, versions), making it clear the tool provides coverage statistics. It distinguishes from sibling listing tools like list_axes or list_dysfunctions by aggregating data rather than enumerating items. However, lacks an explicit verb like 'retrieve' or 'get'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool versus alternatives. With siblings that list specific data (e.g., list_axes, list_dysfunctions), the description should clarify that review_stats is for aggregate coverage numbers, not detailed lists. No when-not-to-use or prerequisite info given.

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 discloses that the tool returns a rubric and mentions future behavior, but it does not state whether this is a read-only operation, any required permissions, or what the rubric contains. Side effects are not addressed.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two concise sentences with no redundancy. The first sentence states the core purpose, the second provides future context. Every word 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 tool's simplicity (2 params, no output schema), the description covers the basic functionality. However, it misses details like return format or how the rubric is structured, and the version note may confuse an agent about current behavior.

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 description adds contextual meaning beyond the schema property names: 'observations' are the observations to apply, and 'dysfunction_id' identifies the dysfunction. However, it does not specify format, constraints, or examples. With 0% schema coverage, this is moderate value.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'Return' and the resource 'severity rubric' for a dysfunction and observations. It is specific enough to distinguish from sibling tools like get_dysfunction or list_dysfunctions, though the term 'rubric' could be more precise.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies that this tool is used to retrieve a rubric for scoring, but it does not explicitly state when to use it versus alternatives or provide any 'when-not' guidance. The version mention is forward-looking but not actionable.

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 that the tool returns tiered interventions and contraindications, weighted by evidence_strength. It does not mention side effects, auth, or error handling, but the core behavior 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.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, front-loaded with the action and output. No redundant information. Every clause 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 no output schema, the description provides high-level structure (tiers, contraindications, evidence_strength) but lacks details on output format, pagination, or behavior for missing inputs. It is adequate but not fully complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema has 0% description coverage. The description only clarifies dysfunction_id as input, but does not explain severity (which has enum values). The meaning of severity in relation to tiering is missing, leaving semantical ambiguity.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool returns tiered interventions and contraindications for a dysfunction, defining first-line and second-line. It is distinct from sibling tools like differential_diagnosis and get_dysfunction, which focus on diagnosis or dysfunction details.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is implied for obtaining interventions after identifying a dysfunction, but there is no explicit when-to-use or when-not-to-use guidance relative to sibling tools. The description does not mention use cases or prerequisites.

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