GovernanceOracle - 10 governance tools: board packs, policies, attestations, evidence.
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Average 2.9/5 across 11 of 11 tools scored.
Most tools target distinct resources, but board_report and board_report_llm both generate board summaries, and annual_review and framework_review overlap in the annual review process. Descriptions help differentiate, but there's potential for confusion.
Naming is inconsistent: list_findings and register_finding use verb-first patterns while others use noun-first patterns (e.g., action_tracker, control_status). Mixed conventions make it harder to predict tool names, though they are still readable.
11 tools is well-scoped for a governance/compliance server, covering findings, actions, exceptions, dashboards, and reports without excessive fragmentation.
The tool set covers the main governance workflows—findings, actions, exceptions, and reporting—but lacks update/delete operations for findings and actions, which could be a gap in lifecycle management. Core coverage is present, but some dead ends exist.
action_trackerCInspectTrack remediation actions with deadlines and ownership.
| Name | Required | Description | Default |
|---|---|---|---|
| add | No | Set true to add a new action | |
| owner | No | ||
| title | No | ||
| due_date | No | ||
| priority | No | ||
| finding_id | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description bears full responsibility for disclosure. It fails to indicate that the tool likely creates a new action (via the 'add' parameter), any side effects, permission requirements, or response behavior. The passive verb 'track' masks the mutation, making the tool's behavioral profile unclear.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise sentence with no filler words. It is appropriately sized for a high-level overview, though its brevity limits the amount of useful information conveyed.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has 6 parameters, no output schema, no annotations, and many siblings. The description only gives a vague purpose and two attribute hints, leaving out critical details like how the tool is invoked, what the response looks like, and how it relates to other tools. This is inadequate for a tool of this complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is only 17% (only 'add' is described). The description mentions 'deadlines and ownership', which gives some meaning to due_date and owner, but title, priority, and finding_id remain unexplained. It does not sufficiently compensate for the low schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool tracks remediation actions, which is a clear verb+resource, but 'track' is ambiguous—it doesn't specify whether the tool adds, lists, or updates actions. The schema's 'add' parameter implies a create operation, yet the description doesn't align with that, making the exact purpose unclear.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives no guidance on when to use this tool versus siblings like 'register_finding' or 'list_findings'. There is no mention of workflows, prerequisites, or alternative tools, leaving the agent without context for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
annual_reviewBInspectAnnual framework review evidence bundle — checklist, stats, Art. 6(5) compliance.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of disclosing behavior. It describes the content ('checklist, stats, Art. 6(5) compliance') but does not state whether the tool reads, creates, or modifies data, nor its permissions or side effects. 'Evidence bundle' hints at a read-only output but is not definitive.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded line with no wasted words. It packs the tool's identity ('annual framework review'), output type ('evidence bundle'), and key contents ('checklist, stats, compliance') into a compact phrase.
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 an output schema and annotations, the description should provide more structural detail. It tells what the bundle contains but not the output format (e.g., JSON, PDF, link), nor whether this is a retrieval action. It is minimally complete for a 0-param tool but leaves ambiguity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so there is nothing to document. The description's content is consistent with the empty schema, and with no params requiring explanation, the baseline of 4 applies.
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 identifies a specific resource ('evidence bundle') and its scope ('annual framework review'), clearly distinguishing it from sibling tools like framework_review. However, it lacks an explicit action verb (e.g., 'retrieve', 'generate'), making the intended operation somewhat implicit.
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 phrase 'Annual framework review' implies the tool is for annual review contexts, and the mention of 'Art. 6(5) compliance' gives specific relevance. But there is no explicit when-to-use/when-not-to-use guidance, nor mention of alternatives, so usage context is implied rather than stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
board_reportBInspectGenerate Management Body review pack — executive summary, open findings, risk posture, overdue items.
| Name | Required | Description | Default |
|---|---|---|---|
| period | No | Reporting period (e.g., 'Q1 2026') |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations were provided, so the description carries the full burden of behavioral disclosure. It identifies the output components but does not disclose side effects, permissions, or other behavioral traits beyond generating the pack. It is unclear if the operation is read-only or has any side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, focused sentence that gets straight to the point, with no unnecessary words. It is well-structured and front-loaded with the verb 'Generate'.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers the tool's primary function and output components, but it does not explain the output type/format or distinguish it from the sibling tool board_report_llm. Given the existence of a similarly named sibling and the lack of an output schema, more details would improve completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema already describes the 'period' parameter at 100% coverage, so the description does not need to repeat it. The description adds no additional meaning about the parameter beyond what is in the schema, so baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool generates a Management Body review pack and lists its contents (executive summary, open findings, risk posture, overdue items). However, it does not differentiate from the sibling tool board_report_llm, which appears to be a similar tool, so it lacks sibling distinction.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. There is no mention of preferred scenarios, exclusions, or comparison with sibling tools like board_report_llm.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
board_report_llmAInspectAI-powered board executive summary using local Gemma 4 LLM. Generates narrative summary from current findings in German or English.
| Name | Required | Description | Default |
|---|---|---|---|
| language | No | Language: de or en | en |
| additional_context | No | Extra context to include (deadlines, events) |
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. It discloses that it is AI-powered and uses a local Gemma 4 LLM, which are useful behavioral traits, but it does not mention whether it is read-only, potential latency, non-determinism, or any system dependencies. This is a moderate gap given the lack 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a compact two-sentence statement that front-loads the core function and includes only relevant detail about language choice. There is minimal redundancy and no wasted wording, making it appropriately concise.
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 report generation tool with two optional parameters, the description covers the main purpose, the AI aspect, and language support. It does not specify the output format or further behavior, but since the output is implied to be a narrative summary, this is adequate. A bit more detail about the return format could improve completeness.
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 input schema already documents both parameters (language and additional_context) with clear descriptions, giving 100% schema coverage. The description adds no extra parameter semantics beyond echoing 'German or English', so the baseline of 3 applies.
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 generates an AI-powered board executive summary using a local Gemma 4 LLM, with the verb 'Generates' and a specific resource. It distinguishes itself from the sibling tool 'board_report' by explicitly mentioning AI and the local LLM.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context by indicating it is for creating narrative board summaries, and notes the language options. However, it does not explicitly state when to prefer this over the traditional 'board_report' or other alternatives, nor does it mention any exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
control_statusBInspectControl effectiveness dashboard across all DORA control domains.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full responsibility for disclosing behavior. 'Dashboard' implies a safe read-only operation, but the description doesn't confirm this, nor does it mention data sources, freshness, or what the output looks like. It is minimally transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence that front-loads the core purpose. Every word is meaningful, with no redundancy or filler.
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 (no parameters, no output schema), the description is arguably adequate for basic understanding. However, it lacks contextual differentiation from sibling dashboard tools and doesn't explain what 'control effectiveness' entails, which limits an agent's ability to select it appropriately among alternatives.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so the description need not add parameter meaning. The baseline of 4 is appropriate here since there is no schema information to supplement.
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 identifies the tool as a 'control effectiveness dashboard' across 'DORA control domains', which states the resource and scope. While it doesn't explicitly use a verb like 'show' or 'view', 'dashboard' implies a read-only reporting function. It distinguishes from siblings like kpi_dashboard and board_report by focusing specifically on control effectiveness.
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 is provided on when to use this tool versus alternatives. The description merely states what it is, with no mention of use cases, exclusions, or relationships to sibling tools such as health_check or exception_register.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
exception_registerCInspectView or add risk acceptances / exceptions with expiry tracking.
| Name | Required | Description | Default |
|---|---|---|---|
| add | No | Set true to add a new exception | |
| title | No | ||
| finding_id | No | ||
| accepted_by | No | ||
| expiry_date | No | ||
| acceptance_date | No | ||
| risk_description | No | ||
| compensating_controls | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of disclosing behavioral traits. It notes the tool can 'view or add' exceptions and mentions 'expiry tracking,' but it does not explain side effects of adding an exception, validation requirements, potential conflicts, or behavior on duplicate entries. The transparency is minimal and leaves significant gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence, which is concise, but it is under-specified for a tool with eight parameters. It omits critical operational details, making it too terse to be genuinely useful. It is not unnecessarily verbose, but it does not fully earn its place given the complexity.
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 8 parameters, no output schema, and no annotations, the description is grossly inadequate. It provides no information about parameter interdependencies, return values, or operational nuances. The description only covers the surface-level action, leaving the AI without enough context to use the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is only 13% (only 'add' has a description). The tool description adds no parameter-level meaning beyond the existing schema, and it does not explain the purpose of fields like title, finding_id, accepted_by, or expiry_date. With eight parameters and no compensating details, the description utterly fails to clarify parameter semantics.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: 'View or add risk acceptances / exceptions with expiry tracking.' This specific verb+resource pairing distinguishes it from sibling tools like list_findings or register_finding, which focus on findings rather than exceptions. The mention of 'expiry tracking' adds useful scope context.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. It does not mention any prerequisites, exclusion criteria, or direct comparisons to sibling tools. The description merely states what it does, leaving the AI to infer use cases from the tool name and context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
framework_reviewCInspectICT Risk Management Framework annual review — effectiveness assessment per DORA area.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There are no annotations, and the description gives no behavioral details. It does not disclose whether the tool is read-only, generates a report, modifies data, requires special permissions, or has any side effects. The word 'review' might imply a read operation, but the description does not confirm this.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise sentence with no filler or redundancy. It front-loads the core purpose and is easy to parse. It earns a high score for efficiency.
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?
Despite the low complexity (no params, simple schema), the description is too sparse. It does not mention what the tool returns or produces, what 'effectiveness assessment' involves, or how it differs from the similarly named sibling 'annual_review'. A bit more context about the outcome would make it 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?
The tool has zero parameters and an empty schema, so there is no parameter information to explain. Per the baseline rule for 0-parameter tools, the description does not need to compensate for missing schema details, and it appropriately omits parameter syntax.
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: it performs an annual review and effectiveness assessment of the ICT Risk Management Framework, scoped per DORA area. This is a specific verb+resource combination ('review', 'effectiveness assessment'), but it does not explicitly distinguish from the sibling tool 'annual_review', which likely has a similar function.
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 is provided on when to use this tool versus any of the siblings. The description only states what the tool does without mentioning prerequisites, alternatives, exclusions, or typical scenarios. This leaves the agent unsure about tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
health_checkCInspectServer status.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full responsibility for disclosing behavior. 'Server status' gives no information about side effects, return format, or what specific checks are performed. The agent cannot infer whether this is a read-only operation 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely short, but it is also under-specified. 'Server status' is a fragment rather than a complete sentence, and while there is no fluff, it fails to provide enough substance to be useful. This is under-specification rather than effective conciseness.
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 should explain what the status entails or what the agent should expect. It does neither. For a zero-parameter tool, the description may not need much, but it still leaves critical gaps about the response and use case.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, and the schema is empty, so there are no parameter semantics to explain. According to the rubric, a baseline of 4 applies when there are no params, and the description does not need to compensate.
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 'Server status' clearly identifies the tool's function: it relates to checking the server's health. Although it lacks an explicit verb, the noun phrase is unambiguous and distinguishes this tool from siblings like 'action_tracker' and 'kpi_dashboard', which serve different purposes.
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 is provided on when to use this tool. It does not mention prerequisites, typical invocation scenarios, or alternatives. This leaves the agent without context for choosing it over other tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
kpi_dashboardBInspectICT Risk KPIs — open findings, overdue rate, remediation rate, exception count.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There are no annotations, so the description carries full responsibility for disclosing behavior. It does not mention whether the tool performs a read-only operation, requires special permissions, or how it returns the KPIs (e.g., response format, pagination). This is a significant gap for a tool with no other metadata.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise and front-loaded with the tool's purpose. It lists specific KPI names, avoiding filler language. However, it reads more like a label than an actionable sentence, which slightly reduces structure clarity.
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 zero-parameter tool with no output schema, the description provides a useful summary of the KPI categories but does not clarify what the tool actually returns (e.g., a report, a JSON object) or whether it is a live dashboard. This leaves some ambiguity for an agent deciding to invoke it.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool takes zero parameters, and the schema is empty. Since there are no parameters to document, the baseline of 4 applies. The description adds no parameter-specific details because none exist.
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 indicates a KPI dashboard for ICT risk, listing specific metrics like open findings and overdue rate. It lacks a verb, and sibling differentiation relies mainly on the name rather than explicit distinction, but the content is clear.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives such as control_status, list_findings, or board_report. The description merely states what KPIs are included, not when or why to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_findingsCInspectList findings with optional filters.
| Name | Required | Description | Default |
|---|---|---|---|
| owner | No | ||
| domain | No | ||
| status | No | ||
| severity | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full responsibility for behavioral disclosure. It only states the basic action and does not mention pagination, sorting, permissions, or what is returned. No contradictions with annotations exist.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence with no filler or redundancy. Every word earns its place, making it highly concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has no output schema or annotations, so the description must provide sufficient context. It does not explain what findings are, how filters are applied, or what the response looks like. This is inadequate for a tool with four filter parameters.
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 input schema has 0% description coverage, and the description adds no meaning to the parameters beyond the phrase 'optional filters'. It does not explain what each filter does or how they interact, failing to compensate for the schema's lack of documentation.
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 'List findings with optional filters' clearly states the action (list) and resource (findings), distinguishing it from sibling tools like register_finding which is a create operation. However, it does not explicitly name alternatives or further differentiate from other listing tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance on when to use this tool versus other sibling tools. The description does not mention use cases, prerequisites, or situations where other tools 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.
register_findingCInspectRegister an audit finding, risk, or control gap.
| Name | Required | Description | Default |
|---|---|---|---|
| notes | No | ||
| owner | No | ||
| title | Yes | ||
| domain | No | ||
| source | No | audit, pentest, incident, self-assessment | |
| status | No | ||
| due_date | No | ||
| severity | Yes | ||
| finding_id | No | ||
| description | No | ||
| evidence_ref | No | ||
| related_article | No | ||
| remediation_plan | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose side effects, permissions, reversibility, duplicate handling, and return values. It merely says 'register' without explaining consequences or required authorization. This is insufficient for a mutation tool with no annotation support.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single front-loaded sentence with no filler. It uses the verb 'register' directly and is efficient. However, it sacrifices clarity for brevity, which is why it does not receive a 5.
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 13-parameter tool with no annotations and no output schema, the description is severely under-specified. It does not explain required fields, domain values, statuses, or workflow context, making it nearly impossible to use correctly without external documentation.
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 only 8% (1 of 13 parameters), leaving most parameters undescribed. The tool description itself does not mention any parameters, failing to compensate. Users must rely on parameter names and the sparse schema descriptions, which is not enough for a 13-parameter tool.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Register an audit finding, risk, or control gap' clearly indicates a creation/recording action with a specific resource. It distinguishes from list_findings (read-only) but vaguely groups three entity types, creating some ambiguity about whether these are distinct registration paths. It earns a 4 rather than a 5 because it does not explicitly differentiate from alternative write tools like exception_register.
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?
There is no guidance on when to use this tool versus alternatives. It does not mention scenarios, prerequisites, or acceptable use cases, nor does it compare against list_findings or exception_register. The absence of any usage context is a clear gap.
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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Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
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