mcp-cli-tools
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation2/5
Several tools overlap in functionality: run_antigravity and consult_model both query Antigravity, while run_codex overlaps with review_workspace and security_audit. The descriptions explicitly say to prefer other tools for new workflows, creating confusing boundaries.
Naming Consistency3/5Most tools follow verb_noun (run_antigravity, consult_model, compare_models, review_workspace), but security_audit, adversarial_check, and fugu_orchestrate break the pattern with noun_noun, adjective_noun, and noun_verb structures. All are lowercase snake_case, so it remains readable.
Tool Count5/5With 8 tools, the set is well-scoped for an AI CLI toolkit covering model queries, comparisons, reviews, audits, and orchestration. Each tool addresses a distinct workflow area, and the count is within the ideal range.
Completeness4/5The toolkit covers core workflows: model consultation, comparison, adversarial checking, code review, security audit, and orchestration. Minor gaps exist, such as no tool for listing available models or directly executing code, but the primary surface is solid.
Average 3.7/5 across 8 of 8 tools scored. Lowest: 2.9/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 3 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 must disclose behavioral traits. It only mentions the use of codex review, but fails to state whether the audit modifies files, requires permissions, or what the output/return behavior is.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise, front-loaded sentence that avoids fluff. While it under-specifies context, it is structurally efficient and easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no annotations, no output schema, and only a minimal description, the tool lacks important contextual information. Users cannot tell what to expect in return, when to use it, or what side effects may occur.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
All four parameters have schema descriptions covering 100% of properties. The tool description does not add any additional parameter semantics beyond the schema, so the baseline of 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states a specific action: running a security audit via codex review with a specialized prompt. This distinguishes it from sibling tools like run_codex or review_workspace, though it does not elaborate on the audit's exact scope or outputs.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does 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. No mention of appropriate contexts, prerequisites, or exclusions relative to sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the burden of explaining side effects and runtime behavior. It mentions local execution and shared memory, but omits details about disk writes, network calls, rate limits, or the final synthesis result. The description gives a high-level overview but lacks depth for a tool that likely manages multiple workers and shared state.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that packs the key mode distinction. It is front-loaded and contains no filler, making it concise and easy to read.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite the tool's complexity (9 parameters, two enums, no output schema), the description gives only a brief overview. It does not explain return value shape, error scenarios, or how the workflow synthesis is structured. The description leaves many operational details unaddressed, making it incomplete for such a feature-rich tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% parameter descriptions, so the baseline is 3. The description adds a little extra meaning by linking 'memoria compartilhada' to shared memory and 'sintese' to synthesis, but it also introduces terms like 'model_id', 'subtasks', and 'access_list' that are not present in the schema, which could confuse. Overall, the schema already does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool executes a Fugu-style local orchestration with two modes (fast and ultra). It differentiates the modes, and the verb 'Executa' plus the resource 'orquestracao local estilo Fugu' identifies the purpose. It does not explicitly contrast with sibling tools, but the orchestration role is distinct enough.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description conveys that fast mode selects a worker and ultra mode creates a multi-agent workflow, giving some guidance on when to use each mode. However, it provides no explicit exclusions or alternatives, and does not discuss prerequisites or integration context relative to sibling tools like run_antigravity or compare_models.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the full transparency burden; it only reveals that an external model is asked to attack the proposal. Side effects, external-call costs/failures, return format, and permissions/requirements are left unspecified, which is a thin disclosure for a networked tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence, front-loaded with the exact action and target, with no filler or repeated schema content. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description clearly states the core function and gives a hint of the output ('apontar falhas concretas'), but with no output schema and no annotations it leaves out return structure, behavior/usage caveats, and failure-handling context. It is minimally complete but has clear gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema already covers all five parameters with descriptions, so the baseline 3 applies. The tool description adds no additional parameter-level meaning beyond restating the proposal as 'proposta, plano ou solucao'.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Pede... atacar') and names the resource ('proposta, plano ou solucao') with a clear outcome ('apontar falhas concretas'). The external-model/adversarial angle distinguishes it from siblings like review_workspace or security_audit.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The intended use is implied by the purpose—adversarial critique of a proposal by an external model—but there is no explicit guidance about when to choose this over siblings like consult_model or review_workspace. No when-not-to-use or alternative recommendations 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?
With no annotations provided, the description carries the transparency burden. It discloses that only safe queries are executed, implying read-only behavior, but doesn't detail authentication, state changes, or error behaviors. The 'safe queries only' statement adds some context, but more would be expected.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise at two sentences and immediately states its scope. However, the opening 'Compatibilidade:' is an odd label rather than an action verb, which slightly reduces clarity, but overall it is compact and to the point.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given six parameters, no output schema, and no annotations, the description is too sparse. It does not explain return values, how cwd/args are handled, or what the subcommands (models, help, changelog) output. It covers the tool's basic intent but lacks sufficient behavioral and operational detail for a complex tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so all parameters are documented within the schema. The tool description adds no additional parameter semantics beyond the schema, which is the baseline for high coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description states it executes safe queries from the Antigravity CLI, which is a specific verb+resource. It distinguishes itself by explicitly mentioning that prompt-related tasks should use consult_model or compare_models, though it does not list the exact safe subcommands.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
It gives explicit guidance: use this for safe queries, and for prompts prefer consult_model or compare_models. This clearly defines when to use the tool versus alternatives, satisfying the dimension fully.
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 the parallel execution and model count range, which is helpful, but omits details like potential costs, timeout behavior, or result aggregation, leaving some behavioral aspects unclear.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, focused sentence that front-loads the core action and purpose, with no filler or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has no output schema and no annotations, yet the description provides only the basic purpose. It does not clarify what the user should expect as output or how to interpret consensus/divergence, which is a notable gap for a comparison tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 75%, so most parameters are already described. The description's mention of '2-4 modelos' reinforces the models bound but adds no new semantic detail beyond the schema's minItems/maxItems.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action (sends the same question), the resource (2-4 Antigravity models in parallel), and the purpose (second opinion, consensus or divergence). This distinguishes it from siblings like run_antigravity or consult_model, which likely handle single-model interactions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when to use the tool by mentioning 'second opinion, consenso ou divergencia', signaling it is for scenarios needing multiple perspectives. However, it does not explicitly name alternative tools or provide exclusion criteria, so it falls 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?
With no annotations, the description carries the full burden. It discloses non-interactive execution and the output format (raw text + structured metadata), which is valuable. However, it does not mention potential side effects like network calls, cost, or failure modes, so transparency is incomplete. Score 3.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence that efficiently conveys the action, mode, and output. There is no wasted wording, so it earns a 5 for conciseness and structure.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has moderate complexity (4 parameters, 1 required, no output schema, no annotations). The description gives a high-level overview but lacks specifics about the metadata structure and parameter interactions. It is adequate but not fully comprehensive, so score 3.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema provides descriptions for 100% of the parameters, so the baseline is 3. The description itself does not add parameter semantics beyond the schema, so it appropriately stays at the baseline.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Consulta um modelo externo via Antigravity em modo nao interativo e retorna texto bruto mais metadata estruturada' clearly states the tool's purpose: it consults an external model via Antigravity in non-interactive mode and returns raw text plus structured metadata. This specific verb+resource+scope distinguishes it from siblings like run_antigravity (which may be interactive) and run_codex.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives clear context: this tool is for non-interactive consultation of an external model via Antigravity. It implies usage but does not explicitly name alternatives or when-not-to-use compared to siblings, so it lacks exclusions. This earns a 4.
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 of behavioral disclosure. It states 'escopo seguro' (safe scope), implying a non-destructive review of workspace changes, but it does not disclose return value, side effects, prerequisites, or any resource implications. This is adequate but incomplete.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that is front-loaded with the core action and immediately explains the safe scope options. There is no filler or redundant content.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 7 parameters, no output schema, and no annotations, the one-sentence description captures the essential purpose and scope modes but omits expected return details and operational prerequisites. This is minimally viable but leaves clear gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema provides descriptions for all 7 parameters, giving 100% coverage, so the baseline is 3. The description mentions the three scope modes, echoing the schema's 'scope' parameter, but does not add meaningful detail beyond what the schema already documents for each parameter.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool executes 'codex review' with a specific safe scope (uncommitted, base diff, or commit). The verb 'Executa' and resource 'codex review' are specific, and it differentiates itself from sibling tools like run_codex by focusing on safe workspace review rather than general codex execution.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context on when to use the tool by listing the supported scopes (uncommitted, base, commit), which signals appropriate use cases. However, it does not explicitly name alternatives or exclusions relative to sibling tools, so it earns a 4 rather than 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?
With no annotations, the description carries the behavioral transparency burden. It discloses the safety constraint (only safe codex review flags), which is useful, but does not mention side effects, permissions, return behavior, failure modes, or other behavioral traits beyond the schema.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with the core compatibility statement and an actionable alternative. There is no redundancy or filler; every word contributes to meaning.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the rich schema and clear scope limitation, the description is contextually complete for a compatibility wrapper. It explains the narrow use case and directs new workflows to alternatives, though it omits some operational details like result format or required environment, which are less critical here.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the schema fully documents all parameters. The description adds no parameter-specific semantics beyond what the schema already provides, such as the safe flags or the 'action' constraint.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: 'executa apenas codex review com flags seguras' (runs only codex review with safe flags). It identifies the specific verb, resource, and scope, and distinguishes it from siblings by restricting to codex review and pointing to alternatives.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly says to prefer 'review_workspace ou security_audit' for new workflows, providing clear when-to-use guidance and named alternatives. This directly helps an agent choose the correct 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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