SkullRender-Agents MCP Server

by crozzbite

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Latest release: v0.1.0
Disambiguation5/5
Each tool targets a distinct resource and operation: listing/getting agents, listing/getting packs, resolving identities, and validating/retrieving schemas for briefs. There is no overlap or ambiguity between them.
Naming Consistency4/5
All tools share the 'skflow_' prefix and mostly follow a resource_action pattern. The only deviations are 'skflow_brief_schema' lacking an explicit verb and the singular/plural inconsistency between list and get (e.g., 'agents_list' vs 'agent_get').
Tool Count5/5
With 7 tools, the server is well-scoped for its purpose—covering agent manifests, personality packs, identity resolution, and brief validation—without being bloated or too sparse.
Completeness5/5
The tool set provides a complete read-oriented workflow for the domain: list and get for both agents and packs, a resolution function, and validation with its accompanying schema. No obvious gaps or dead ends are apparent.
Average 4/5 across 7 of 7 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 5 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
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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?
No annotations are present, so the description carries full burden. It does not disclose whether the operation is read-only, requires permissions, or has side effects. The phrase 'Injected on-demand into offices' is ambiguous and could be misread as an effect of calling the tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the core action, and provides useful examples without any fluff.
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?
As a simple zero-parameter list tool, the description is largely complete. It lacks explicit return-format details (e.g., list of names vs. full objects), but the given examples help set expectations.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so schema coverage is 100% and the parameter bar is lowered. The description adds value by explaining what the list contains, fulfilling the baseline of 4.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('List') and the resource ('Legion personality packs'), and even names examples. It distinguishes from siblings like skflow_pack_get by its list-oriented verb.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Usage is implied ('use when you need to list packs'), but no explicit guidance on when to use this versus siblings like skflow_pack_get or skflow_agents_list is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions that the tool returns 'full YAML text,' which adds return-format context, but it doesn't disclose error behavior, authentication needs, or side effects. Since it's a simple read operation, this is adequate but not rich.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is exactly two sentences, front-loaded with the core action, and contains no fluff. Every word earns its place, and the cross-reference to the list tool is efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple one-parameter get tool with no output schema, the description covers the purpose, the return type (YAML text), and how to obtain valid ids. It is reasonably complete, though it could optionally mention error behavior for invalid ids, but that is not critical for basic usage.
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 input schema already fully describes the single 'id' parameter as 'Manifest id field' (100% coverage). The description merely says 'by id,' which adds no new semantics beyond what the schema provides, so the baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb ('Get'), a specific resource ('agent manifest'), and the scope ('full YAML text by id'). It also distinguishes from sibling tools by pointing to skflow_agents_list for available ids, making the tool's role clear.
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 usage context: it tells the agent to use skflow_agents_list to get available ids before calling this tool. However, it doesn't explicitly state when not to use this tool or mention alternatives beyond the list tool, so it falls short of a perfect 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the transparency burden. The verb 'List' implies a read-only operation, which is a useful behavioral signal. However, it does not disclose whether the list is paginated, what fields a manifest contains, or whether authentication/authorization is required. Since it is a simple list, the implied behavior is somewhat sufficient, but additional details would improve transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is one short sentence that conveys the essential purpose and even enriches it with the list of manifest types. There is no unnecessary wording or repetition of the tool name.
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-argument list tool, the description provides adequate context: it identifies what is being listed and the categories included. It does not describe the return payload format, but with no output schema, a slightly richer description of the output structure could make it fully complete. Overall, it is sufficient for the tool's simplicity.
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 0 parameters, and the schema is empty with 100% coverage. Per the baseline rule, a 0-parameter tool receives a 4 because there is no parameter ambiguity to resolve. The description does not need to add parameter semantics.
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 resource ('declarative agent manifests') and enumerates the types of agents covered (spine / Saep / Sae / legacy expertos). This clearly distinguishes it from sibling tools like skflow_agent_get (get a single agent) and skflow_packs_list (list packages).
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 implied usage is clear: use this tool to list agent manifests. However, there is no explicit comparison to alternatives or exclusions, such as 'use skflow_agent_get to fetch a specific agent' or 'this does not include packaged flows.' Guidance beyond the core purpose is absent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It states the tool returns a JSON Schema, which implies a read-only operation, but it does not explicitly disclose safety, side effects, or response structure beyond the schema. For a simple retrieval tool this is adequate, but minimal.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, well-structured sentence that front-loads the action ('Return the JSON Schema') and adds the audience context. Every word earns its place, with zero 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?
Given the tool's simplicity (no parameters, no output schema), the description is complete enough. It specifies the output type (JSON Schema) and its purpose, and the sibling list helps locate it among related tools. Minor gaps like not mentioning that the schema is standard JSON Schema or how it might be used are not significant for a metadata tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so with schema coverage at 100%, no parameter explanation is needed. The description correctly avoids mentioning parameters, and the baseline of 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 clearly states the tool returns the JSON Schema used for validating Presentador⇄Orquestador briefs. It names the specific resource and distinguishes it from sibling tools like skflow_brief_validate, which likely performs validation rather than returning 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 intended audience ('for prompting engineers') implies when to use this tool, but there is no explicit comparison with alternatives like skflow_brief_validate. The usage context is clear but not elaborated with exclusions or 'when not to use' guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the burden of behavioral disclosure. It does convey that the tool is deterministic, schema-mirroring, and non-LLM, implying a safe, repeatable validation. However, it does not disclose whether the tool has side effects, what happens on invalid input (e.g., errors vs. boolean result), or whether special permissions are needed. These gaps leave the agent without full transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences front-load the action and essential qualifiers ('Presentador→Orquestador', 'deterministic', 'no LLM'). Every word earns its place, with no repetition of schema details or filler.
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?
The tool is simple (1 parameter, no nested objects, no output schema), and the description covers its main purpose and input types. However, without an output schema, the description does not clarify the return format or how success/failure is signaled. This is a minor gap for an otherwise straightforward validation tool, so it earns a 4 rather than 5.
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 input schema already covers the sole parameter 'brief' with 100% coverage, describing allowed types (object or string) and the structure (goal/constraints/forbidden_capabilities). The tool description adds the terms 'YAML/JSON string' and 'Presentador→Orquestador' but does not meaningfully extend beyond the schema. Baseline of 3 applies since schema coverage is high.
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 ('Validate') and resource ('Presentador→Orquestador brief'), and clearly distinguishes from sibling tools like skflow_brief_schema by emphasizing the tool performs a deterministic check against a schema rather than returning the schema itself. The purpose is unambiguous.
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 implies the use case: validate a brief against the schema, with the note 'no LLM' clarifying it is a deterministic, non-AI check. It indirectly contrasts with skflow_brief_schema, but does not explicitly name alternatives or state when not to use it, so it falls just short of a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. It adds a useful behavioral detail: 'Pack defaults from manifest.personality_pack or pack.inject_default_into.' However, it does not disclose error behavior, side effects, or what exactly the resulting prompt block contains, leaving some ambiguity for the agent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence that wastes no words. It states the primary action immediately, then compacts the optional parameter behavior into a dependent clause. Every phrase earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with two parameters and no output schema, the description gives a clear picture of its purpose and the key default behavior. It could be more complete by explaining the output format of the prompt block or error cases, but given the simplicity and the presence of schema descriptions, it covers the essential context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so parameters are already documented. The description adds value by explaining the default pack resolution logic (from manifest.personality_pack or pack.inject_default_into), which clarifies how the inject_pack parameter interacts with the system's fallback behavior beyond the schema's simple true/false/string description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Resolve'), the resource ('office identity (+ optional personality pack)'), and the output ('a prompt block for Cursor subagents'). This distinguishes it from sibling list/get tools which retrieve raw data, and from brief validation tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies this tool is used when assembling subagent prompt blocks, but it does not explicitly state when to prefer it over sibling tools like skflow_agent_get or skflow_pack_get. There are no exclusion criteria or named alternatives, so the usage context remains implied rather than explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses that the output is 'full YAML' and that it operates by id, implying a read-only operation. However, it does not mention error behavior, permissions, or any side effects, which are not critical for a simple getter but would add transparency.
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 is front-loaded with the verb 'Get', immediately states the resource and scope, and ends with a helpful example. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with one parameter and no output schema, the description is complete: it states what is returned (full YAML), how to specify which pack (by id), and gives an example. It does not need to detail error cases or list alternatives given its simplicity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so the description must compensate. It adds meaning by identifying the parameter as the pack id and providing a concrete example ('PackLich'). This is sufficient for a single simple parameter, though it does not specify id format beyond the example.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action: 'Get full YAML of one personality pack by id' with a concrete example 'PackLich'. It distinguishes this from sibling tools like skflow_packs_list, which lists packs, by focusing on retrieving a single pack's full YAML.
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 for when to use the tool: when you have a specific pack id and need its full YAML. It does not explicitly mention alternatives or exclusions, but the usage context is clear enough for a simple getter tool.
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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