deep-agentic-core-mcp

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by DeepAgentLabs

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Server Quality Checklist

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Latest release: v0.2.0
Disambiguation4/5
Each tool is grouped under a clear domain prefix and has a distinct action, but core.health/core.verify both touch readiness and lens.report_summary/lens.slo_summary/lens.audit_report are all report-like. The descriptions clarify the boundaries well, leaving only minor potential for confusion.
Naming Consistency4/5
Names consistently use lowercase dotted namespaces with snake_case and mostly follow a verb_noun pattern (analyze_workflow, compare_runs, run_experiment). A few noun-only names (core.health, core.version, sidecar.status) break the verb-first pattern, but the convention is still predictable.
Tool Count5/5
Fourteen tools is well within the ideal range and each tool serves a distinct purpose across five focused domains. No tool feels redundant or gratuitous for a server covering core diagnostics, lens analysis, chaos, sidecar, and spec validation.
Completeness4/5
The tool set covers the advertised surface well: health/readiness, workflow analysis, run comparison, SLO evaluation, audit, chaos experiments, sidecar inspection, and artifact validation. Minor gaps exist around creating/modifying/storing artifacts, but they may be outside the server's intended scope and do not create obvious dead ends.
Average 3.7/5 across 14 of 14 tools scored. Lowest: 2.9/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 69 commits in the last 12 weeks
- Last stable release on August 9, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
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```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
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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?
The description does not disclose that analysis likely reads/writes session state (readOnlyHint is false), or what effect invoking it has. 'Analyze' suggests a read-only operation, but the annotations and session_id parameter indicate possible side effects, so the description under-discloses 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 efficient sentence with no redundant wording and the core action front-loaded. It errs on under-specification rather than bloat, but the structure itself is clear.
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?
With no output schema, the description should explain what an analysis produces or returns; it does not. It also omits the session read/write behavior implied by readOnlyHint=false, leaving the agent without enough context to invoke the tool or interpret results safely.
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 clarifies that the 'artifact' property is an AgenticLens-compatible workflow object, adding meaning beyond the bare schema entry. However, it says nothing about session_id semantics and the schema already documents session_id, so the added value is marginal.
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 names a concrete action and target: 'Analyze an AgenticLens-compatible workflow artifact.' This is clearer than a tautology and identifies the resource type, but 'analyze' is generic and does not distinguish it from siblings like spec.validate_artifact or lens.report_summary.
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 statement about when to choose this tool over alternatives such as spec.validate_artifact or lens.report_summary. The only implied trigger is having a workflow artifact to analyze, which is too weak given the overlapping sibling tool names.
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?
The readOnlyHint annotation already communicates a non-mutating operation, and the description's 'Return' is consistent with that. However, the description adds little beyond the annotation, offering no detail about potential size, pagination, performance, or preprocessing 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 efficient sentence with the core verb and object front-loaded. It is concise and scannable, though it may be too terse to provide full context.
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?
With no output schema, nested input objects, and low parameter documentation, the description leaves significant gaps. It does not define the expected 'report' input, the semantics of 'include_html', or the shape of the returned case-by-case details, so an agent would struggle to invoke it correctly.
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 only 33%, with 'report' and 'include_html' lacking descriptions. The description does not explain what the 'report' object should contain, how 'include_html' affects output, or what 'audit trail' means, so it fails to compensate for the schema gaps.
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 action ('Return') and a resource ('case-by-case evaluation detail for an audit trail'), which conveys more than the tool name alone. It hints at a report-level detail view, but does not explicitly distinguish itself from siblings like lens.report_summary or lens.compare_runs.
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?
No guidance is given about when to use this tool versus sibling tools such as lens.report_summary, lens.analyze_workflow, or lens.slo_summary. The phrase 'for an audit trail' implies some context, but there are no explicit conditions, exclusions, or alternatives.
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?
The readOnlyHint annotation already communicates that this is a read-only operation, so the description does not need to restate that. It adds useful context about the SLO/release-gate framing, but it does not explain what actually happens when thresholds are applied, whether a pass/fail result is produced, or how session state factors in.
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, dense sentence with no filler. It front-loads the action and the primary resource, making it easy to scan.
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?
With no output schema, the description should explain what the agent will receive after applying thresholds, but it does not. It also omits how session state is used, what shape the report must have, and what format thresholds take, leaving too much to inference for a nested-parameter 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 low at 33%, with report and thresholds being bare objects. The description partially compensates by identifying report as an evaluation report and thresholds as SLO thresholds, but it does not explain their expected structure, how the thresholds map to report metrics, or the role of session_id beyond the 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 gives a specific verb ('apply') and resource ('release-gate style SLO thresholds to an evaluation report'), so the core purpose is clear. It is implicitly distinct from sibling tools like report_summary or compare_runs, but it does not explicitly name an alternative or contrast itself.
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 this tool should be used rather than lens.report_summary, lens.audit_report, or lens.compare_runs. The phrase 'release-gate style' hints at a scenario but does not state prerequisites, exclusions, or conditions that would route an agent to this tool.
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?
Annotations already declare readOnlyHint=true, and the description is consistent with a read-only validation operation. It adds context about the artifact types and draft version, but it does not disclose whether validation is structural, semantic, or what happens for invalid artifacts.
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?
One short sentence with no filler, front-loads the action, and every word adds specificity. This is an appropriate length for the available schema and annotation context.
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?
With no output schema and no description of return values or error behavior, an agent cannot predict what the tool will provide after invocation. The single-parameter schema is simple, but validation semantics and result shape are left unspecified.
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 describes artifact only as an object with 0% coverage, so the description's 'workflow or run artifact' adds meaningful type context. However, it still does not describe any expected fields or constraints inside the artifact object, leaving the agent with limited ability to construct or inspect it.
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 (validate), a specific resource (workflow or run artifact), and a target standard (AI Operations v0.4 draft). It does not explicitly distinguish itself from siblings like core.verify or lens.analyze_workflow, but the artifact-and-draft scope is distinctive enough.
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?
No guidance is given on when to use this tool versus core.verify or lens.analyze_workflow, and there are no exclusions or prerequisites. The only contextual clue is the spec namespace, which the agent must infer.
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?
Annotations only include readOnlyHint=false, so the description should disclose side effects. It mentions 'Render' and reuse of the session's stored workflow, but does not clarify whether the summary is written back to session state, whether recommendations have side effects, or what exactly happens when the artifact is omitted.
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 conveys both the core purpose and the key parameter behavior without any filler or redundancy. Every word contributes to understanding the tool.
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?
There is no output schema, and the description only says a Markdown report is rendered. It does not describe the return format, what a 'recommendation summary' contains, or how the report is delivered, so an agent has enough for basic invocation but not for fully anticipating the tool's behavior.
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 valuable meaning to the 'artifact' parameter by noting it can be omitted and will reuse the stored workflow. The schema already documents session_id, but the artifact's internal structure and expected contents are left unspecified, and schema description coverage is only 50%, so the description only partially compensates.
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 action ('Render') and a clear resource ('Markdown workflow report and recommendation summary'). It is distinct enough from sibling tools like lens.analyze_workflow, though it does not explicitly name the alternatives it differs from.
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 only usage guidance is that 'artifact' may be omitted to reuse the session's stored workflow. This helps with parameter choice but does not explain when to choose this tool over related reporting tools such as lens.audit_report or lens.slo_summary, leaving selection 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?
Annotations only indicate readOnlyHint=false, leaving room for side effects. The description adds useful behavioral context by mentioning that baseline/candidate can reuse the session's stored runs, but it does not disclose whether the comparison writes results back to session state or what else may be modified.
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 concise and front-loaded, with each sentence adding distinct information: the core purpose and the optionality/reuse behavior. It could be somewhat richer given the sparse schema, but there is no fluff or redundancy.
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 4 parameters, low schema coverage, no output schema, and a non-read-only annotation, this description is not sufficient for an agent to invoke it confidently. It omits what trace runs look like, how regression_threshold behaves, and what the tool returns.
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 only 25% and the description only partially compensates. It clarifies that baseline/candidate can be omitted to reuse stored runs, but the contents of the array item objects and the meaning of regression_threshold are left entirely undocumented.
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 ('Compare baseline and candidate trace runs') and a clear goal ('for regressions'), going beyond the generic title. This clearly differentiates it from sibling analysis/reporting tools like lens.analyze_workflow and lens.report_summary.
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 invocation context: use this tool to detect regressions between baseline and candidate runs. It also explains when parameters may be omitted by referencing reused session state, though it does not explicitly name alternatives or exclusion conditions.
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?
Annotations already declare readOnlyHint=true, and the description is consistent with a harmless read operation. The description adds a bit of context by indicating it returns the supported fault catalog, but it does not disclose output structure or any other behavioral constraints.
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 sentence with no redundancy. The verb and scope are front-loaded, and every word contributes to the tool's meaning.
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, read-only enumeration tool, this description is nearly complete: an agent can tell it is called to obtain supported fault types. It loses one point for not specifying the exact return format, especially since there is no output schema.
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 coverage is trivially complete. Since there are no parameters, the description does not need to add parameter-level meaning, and the zero-parameter baseline 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 names a specific verb ('List') and resource ('supported fault types for chaos experiments'), making the tool's function immediately clear. It does not explicitly differentiate from sibling tools, but the scope is unambiguous and distinct from the nearby chaos.run_experiment.
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 phrase 'for chaos experiments' implies this is a discovery/read tool used when planning or configuring experiments, but it never states when to prefer it over alternatives or whether it should be called before chaos.run_experiment. Usage context is present but only implied.
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?
The readOnlyHint annotation already establishes that this is a read-only operation, and the description aligns by saying 'Inspect'. It adds a little context by specifying that it covers artifacts and call history accumulated over time, but it does not disclose return format, session lifecycle behavior, or error cases. The input schema's 'read/write shared state' phrasing is somewhat ambiguous against the readOnlyHint, though the main description itself is consistent.
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 contributes to identifying the tool's purpose and scope, making it highly concise and easy to parse.
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 read-only inspection tool with one optional parameter and a readOnlyHint, the description provides enough context for an agent to select the tool. It could be slightly more complete by clarifying what a session encompasses or what the returned artifacts look like, especially since no output schema 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 only parameter, session_id, has a complete schema description with 100% coverage, so the tool description does not need to repeat parameter details. The description adds no extra semantic value for the parameter. The schema's phrase 'read/write shared state' is a minor ambiguity given the readOnlyHint, but parameter documentation itself is present.
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 verb ('Inspect') and a concrete resource ('artifacts and call history') scoped to a session. This clearly distinguishes it from sibling tools that report health, validate specs, or run experiments, so an agent can tell what this tool is for 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 Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies that this tool is for reading accumulated session artifacts and call history, but it provides no explicit when-to-use or when-not-to-use guidance and does not name alternatives. The usage context must be inferred from the verb and noun phrase rather than being directly instructed.
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?
Annotations already declare destructiveHint=true and readOnlyHint=false, so the destructive nature is known. The description adds valuable context by saying the target script is 'workspace-sandboxed', which qualifies the scope of potential damage, and that it reports 'resulting events', giving a sense of the output. It does not contradict the annotations and adds meaningful safety context beyond them.
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 sentence that front-loads the core action and safety boundary, followed by the result. Every phrase earns its place, and there is no redundant wording or restating of the tool name.
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 destructive tool with no output schema, the description should clarify what 'events' look like and how to choose faults; it does neither. It also does not point to chaos.list_faults for fault discovery or mention timeout/session defaults. The core contract is clear, but important operational details are left to inference.
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 50%, with script and session_id documented. The description adds only loose meaning for faults ('selected chaos faults') and does not explain what fault strings are valid or how timeout_seconds behaves. It partially reinforces parameter intent but does not fully compensate for the undocumented parameters.
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 ('Run'), a specific resource ('workspace-sandboxed target script'), and the expected outcome ('report the resulting events'). This clearly distinguishes it from sibling tools like chaos.list_faults, which only enumerate faults, and lens.* tools, which analyze runs rather than execute 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 when this tool should be used: when a script should be executed under chaos faults. However, it does not explicitly say when to prefer this over alternatives, nor does it mention chaos.list_faults as the way to discover valid fault names. The intended usage is inferable but not spelled out.
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?
Annotations already declare readOnlyHint=true, and the description adds meaningful output behavior: what categories of diagnostics are returned and that recent successful calls are tracked. It does not contradict the annotations and discloses the tool's operational return scope.
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 purpose and the key return categories without filler. Every word carries meaning, and the most important verb and resource appear first.
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?
With no output schema, the description carries the burden of explaining return values, and it does: adapter availability, loaded resources, and recent successful calls. It is sufficient for a zero-parameter read-only health check, though it does not specify response formatting or interpretation details.
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 schema coverage is effectively complete, so there is nothing for the description to add beyond confirming that no inputs are needed. The baseline of 4 for zero-parameter tools 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 uses a specific verb ('Return') and names the resource ('server diagnostics') with concrete contents: adapter availability, loaded tools/resources/prompts, and recent successful calls. This clearly distinguishes it from siblings like core.version, core.session_state, and sidecar.status.
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?
No guidance is given about when to use this tool versus alternatives such as core.version, core.session_state, or sidecar.status. The word 'diagnostics' implies a health-related use case, but no explicit when/when-not conditions or alternative routing are 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?
The 'current' qualifier indicates the result reflects the present scaffolded state, and 'present in agentic-sidecar' scopes the operation. The readOnlyHint annotation already covers the safety profile; the description adds minor contextual scoping but does not disclose output format or any further behavioral traits.
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 verb and object with no wasted words. It is appropriately sized for a zero-parameter, read-only listing tool.
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 tool with no parameters, a readOnly annotation, and no output schema, the description is complete enough to invoke correctly. It specifies the exact scope (current scaffolded modules, adapters, and integration placeholders in agentic-sidecar), leaving no critical ambiguity for the agent.
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 input schema has zero parameters and the schema description coverage is 100%, so there is no parameter burden for the description to carry. The baseline of 4 applies because no parameter details are needed.
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 ('List') and a concrete resource ('current scaffolded modules, framework adapters, and integration placeholders present in agentic-sidecar'). This clearly distinguishes it from sibling tools like core.health, core.version, or sidecar.status, which concern runtime status rather than inventory of scaffolded components.
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 intended use is implied by the description — call it when you need a listing of scaffolded modules, adapters, or placeholders — but it does not explicitly state when to prefer it over alternatives or when not to use it. No sibling exclusions or usage conditions are mentioned.
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?
Annotations already declare readOnlyHint=true, and the description aligns with that by framing the tool as a report. It adds valuable context beyond the annotation by specifying what 'status' means—importability and runtime readiness—which is more informative than a generic status check.
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, tightly worded sentence that front-loads the verb 'Report' and immediately specifies the subject and the exact conditions being checked. Every word earns its place; no filler or redundancy.
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, read-only status tool without an output schema, the description is nearly complete: an agent knows it will learn whether the sidecar is importable and runtime-ready. It could theoretically specify the return shape more explicitly, but the phrasing is sufficient for a simple boolean-style report.
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 takes zero parameters and the schema is an empty object with 100% coverage, so there are no parameter details for the description to clarify. The description appropriately focuses on behavior and output rather than inputs.
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 ('Report'), a specific resource ('agentic-sidecar'), and two concrete checks (importability and decision runtime readiness). This clearly distinguishes it from siblings like core.health and core.verify, which are more general health checks.
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 use case: check whether the sidecar is ready. However, it does not explicitly compare itself to core.health or core.verify, nor does it give when-to-use vs. alternatives guidance. The intended context is clear but left 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?
The readOnlyHint annotation already covers the read-only nature, so the description only adds the specific targets and the readiness-report outcome. It does not disclose additional behavioral traits such as failure semantics, timeout behavior, or how readiness is computed, but for a simple connectivity check this is minimally adequate.
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?
One sentence, action-first verb, a clean list of targets, and a clear output expectation. Every word earns its place; there is no filler or redundancy.
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 zero-parameter, read-only tool with no output schema, the description is complete. An agent knows exactly what action is performed, which services are involved, and what outcome to expect, so nothing needed for correct 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?
The tool has zero parameters and the schema is an empty object, so schema coverage is trivially complete. With no parameters to explain, the description does not need to add parameter-level detail; baseline 4 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 uses a specific verb ('Check connectivity') and names all four integration targets (agenticlens, agentic-chaos, agentic-sidecar, ai-operations-spec), plus the expected outcome ('report readiness'). This clearly distinguishes core.verify from general health/status tools like core.health or core.version.
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 provides clear context: use this tool when you need to verify connectivity and readiness of the listed integrations. It does not explicitly name alternatives or exclusions, but the scoped list of targets makes the intended use unambiguous.
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?
The readOnlyHint annotation already covers the read-only safety profile. The description reinforces the behavior by stating it returns a value without side effects. For such a trivial zero-parameter operation, there are no hidden behaviors to disclose.
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 sentence with no wasted words. It is direct, front-loaded, and entirely sufficient for the tool's trivial purpose.
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?
Given the tool has no parameters, a read-only annotation, and a simple version-returning purpose, the description fully covers what an agent needs to know to invoke it correctly. No output schema is needed because the return value is inherently simple.
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?
There are zero parameters, so the baseline of 4 applies. The description does not need to clarify any parameter meanings because the input schema is empty.
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 ('Return') and a specific resource ('current server package version'), making the tool's purpose immediately clear. It is easily distinguishable from sibling tools like core.health or core.verify.
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 context is implicitly clear: use this when you need the server package version. However, there is no explicit guidance about when not to use it or which sibling alternative might be more appropriate for related version/health checks.
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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