godmod3-mcp

Server Quality Checklist

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Latest release: v0.1.0
Disambiguation4/5
Most tools have clearly distinct purposes, such as single_chat vs. ultraplinian_chat vs. consortium_chat, and parseltongue_encode vs. parseltongue_detect. Minor overlap exists between health_check and server_info (both relate to connectivity/info) and between dataset_stats and research_stats (both provide statistics but on different datasets), but these are distinguishable with careful reading.
Naming Consistency3/5
Naming conventions are mixed: some tools follow verb_noun patterns (list_models, get_tier, transform_text, submit_feedback, export_dataset), while others use noun_verb or compound nouns (health_check, parseltongue_encode, parseltongue_detect, server_info, dataset_stats). All names are readable and use snake_case, but the inconsistent verb placement and noun-only names prevent a higher score.
Tool Count3/5
With 17 tools, the server falls into the 'heavy' range (16-25). The tool count is justified by the broad scope of the API, covering chat, optimization, text transformation, and research, but it still feels slightly bloated and could be consolidated by merging some informational tools or research stats endpoints.
Completeness4/5
The tool surface is largely complete for the stated domain: it covers chat (single, ULTRAPLINIAN, CONSORTIUM), AutoTune analysis, Parseltongue encoding/detection, STM transformation, feedback, dataset export, and research querying. Minor gaps include no Parseltongue decoding and no dataset upload/management, but these are not critical for the core API usage.
Average 3/5 across 17 of 17 tools scored. Lowest: 2.4/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 16 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
This repository is licensed under AGPL 3.0.
This repository includes a README.md file.
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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?
With no annotations, the description carries the full burden of behavioral disclosure. It only says 'Obfuscate trigger words in text' and does not explain side effects, default behavior, reversibility, or output characteristics.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence, but it is under-specification rather than efficient writing. It provides no structured information beyond a basic purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's four parameters, no annotations, and an output schema, the description is far too sparse for reliable tool selection or invocation. It lacks essential context about parameter usage and behavior.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has four parameters with zero description coverage, and the description does not mention any parameter names or meanings. The agent must guess how 'intensity', 'technique', and 'custom_triggers' affect the output.
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 clearly states the action ('Obfuscate') and the object ('trigger words in text'), distinguishing it from sibling tools like parseltongue_detect. It is a specific verb+resource statement, though it lacks detail on what 'obfuscate' means.
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?
The description provides no guidance on when to use this tool versus alternatives such as transform_text or parseltongue_detect. There are no mentions of preferred scenarios, prerequisites, or exclusions.
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, the description carries full responsibility for behavior disclosure, but it only says 'Submit' without explaining effects, required permissions, return behavior, or impact on the learning loop. This is a significant gap for a mutation-like tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence with no filler, which is good, but it is under-specified. It is not structured to front-load key details beyond the basic purpose.
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?
Given the tool's moderate complexity and lack of annotations, the description is incomplete. It does not explain required inputs, optional behavior, or even the meaning of 'quality feedback' in practical terms, despite an output schema existing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description does not mention any of the five parameters (message_id, rating, context_type, response_text, params). The tool description provides no added semantic meaning beyond the schema's bare property names.
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 clearly identifies the action ('Submit') and the object ('quality feedback for the AutoTune EMA learning loop'). It is specific enough to understand the tool's basic purpose, though it does not explicitly distinguish it from related sibling tools like autotune_analyze.
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?
The description provides no guidance on when to use this tool versus alternatives. It does not mention prerequisites, exclusions, or scenarios where a different tool would be more appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must carry the full transparency burden, but it only reveals that the tool collects responses from multiple models. It does not disclose potential side effects, network costs, latency, or whether it writes to a dataset. Given the tool's complexity and 17 parameters, this is insufficient behavioral disclosure.
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, economical sentence without redundancy. It front-loads the core action. However, it is perhaps too terse for a tool of this complexity, so it loses one point for not using the structure to layer essential details.
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?
Despite having an output schema, the description leaves the tool's context highly under-specified: no explanation of what 'ground truth' means, no integration hints with sibling tools like autotune_analyze or parseltongue_encode, and no prerequisites. For a tool with 17 parameters and no annotation support, this is far from complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 0% description coverage, and the description provides no parameter meaning either. It does not explain the purpose of non-obvious parameters like godmode, parseltongue, or strategy, nor does it clarify the expected format of messages. The description completely fails to compensate for the schema's lack of annotations.
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 clearly states a verb+resource action: 'Collect responses from all tier models and synthesize ground truth.' This distinguishes it from sibling tools like single_chat by emphasizing multi-model aggregation. However, 'ground truth' is somewhat vague, preventing a perfect score.
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 explicit guidance is provided on when to use this tool versus alternatives like single_chat or ultraplinian_chat. The description implies a consensus-oriented use case but lacks any context for prerequisites, exclusions, or criteria. This is a significant gap for a tool with many specialized siblings.
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 present, so the description carries the full burden of behavioral disclosure. It only states that the tool queries with filters; it doesn't disclose whether the operation is read-only, how pagination works, whether authentication is required, or how filter parameters interact.
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, front-loaded sentence with no filler. It is appropriately concise, though the brevity borders on under-specification for a tool of this complexity.
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 7 parameters, no annotations, and overlapping sibling tools, the one-sentence description is insufficient. An output schema exists, but the description fails to cover parameter semantics, usage context, or behavioral traits, making it incomplete for safe invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description adds no meaning to the 7 parameters. 'With filters' is generic and does not explain mode, limit, model, since, until, offset, or category, leaving the agent without essential parameter semantics.
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 clearly identifies a query operation against a 'published research corpus' with filtering, which is a specific verb+resource pairing. It distinguishes itself from sibling research_info and research_stats by the query/with-filters emphasis, though it doesn't explicitly name alternatives.
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?
The description provides no guidance on when to use this tool versus siblings like research_info, research_stats, or dataset_stats. There are no prerequisites, exclusions, or alternative tool mentions; the only signal is the implied filtering use case.
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?
The description mentions the G0DM0D3 pipeline components but does not explain what they do, any side effects, data usage, or permission requirements. With no annotations, this leaves significant behavioral ambiguity.
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, front-loaded sentence with no padding. However, it omits critical details, making it under-specifying rather than optimally concise.
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?
Given the tool's complexity (23 parameters, no annotations), the description is far too sparse. It does not cover when to use, parameter roles, or pipeline behavior; the output schema cannot compensate for missing usage context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0% and the description provides no explanation of the 23 parameters. The pipeline component names correlate with some parameters (e.g., godmode, autotune, parseltongue) but their meanings and usage are not clarified.
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 clearly identifies a single-model chat operation with a specific pipeline ('GODMODE, AutoTune, Parseltongue, STM'). The phrase 'Single-model' helps differentiate from sibling consortium_chat and ultraplinian_chat, though it does not explicitly name alternatives.
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 provided on when to use this tool versus sibling chat tools. The 'Single-model' label implies a use case but does not state exclusions or alternative selection criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description is the only behavioral disclosure. It merely says 'apply modules to text' without mentioning side effects, purity, error behavior, or output characteristics. This is insufficient for an agent to predict the tool's runtime behavior.
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 concise, well-constructed sentence that front-loads the action. There is no filler or redundancy; every word earns its place.
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 two parameters, no annotations, and no parameter descriptions, the description is far too sparse. It leaves ambiguity about how modules are specified, what the transformation entails, and how to interpret the result. The existence of an output schema doesn't compensate for the missing context in the description itself.
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 0%, so the description must compensate for the parameter meaning. It adds the acronym 'STM' and implies 'modules' are transformations, but it doesn't explain valid module values, the effect of the default null, or the structure of 'text'. The meaning is only barely extended 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 uses a specific verb ('apply') and identifies the resource ('text') and the mechanism ('semantic transformation modules'), which distinguishes it from sibling text tools like parseltongue_encode. However, it doesn't explain what STM are or what the result looks like, so it's clear but not fully refined.
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 to use this tool versus alternatives such as single_chat or parseltongue_encode. The description only states what the tool does, providing no exclusions or contextual triggers.
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 must carry the full burden of behavioral disclosure. It mentions parallel execution and best-response selection, but omits critical traits like API key requirements, resource costs, non-determinism, or any side effects. The one-sentence description does not adequately explain the operational behavior of such a complex tool.
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 sentence that is front-loaded and efficient, stating the core functionality immediately. The parenthetical '(ULTRAPLINIAN)' adds minor noise but does not detract significantly. It earns its place by being brief, though it sacrifices essential detail.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the high complexity (19 parameters, no annotations, many mysterious settings), this description is grossly incomplete. It fails to explain key aspects like how models are selected, what 'best response' means, how to configure providers, or what the output structure looks like. The agent would be severely under-informed for a tool of this scale.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0% for 19 parameters, and the description does not mention a single parameter. Cryptic parameter names like 'godmode', 'parseltongue', 'stm_modules', and 'autotune' are left completely unexplained, making it impossible for an agent to choose appropriate values without external knowledge.
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 'Race many models in parallel and return the best response' clearly states a specific action (racing models) and a distinct outcome (best response), which differentiates it from sibling tools like single_chat and consortium_chat. The verb 'race' is vivid and the resource (models) is explicit, making the tool's primary function unambiguous.
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?
The description provides no explicit guidance on when to use this tool versus alternatives such as single_chat or consortium_chat. It only describes what it does, leaving the agent to infer usage context from the name and siblings, which is insufficient without further elaboration.
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 present, so the description carries the full burden of behavioral disclosure. It only states it 'analyzes' and 'gets parameters,' but does not disclose whether the operation is read-only, requires network calls, involves side effects, or has any auth/rate-limit implications. This is a significant gap for an unannotated 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 a single, concise sentence that front-loads the core action ('Analyze a message'). There is no filler or redundant content, meeting the standard of concise and efficient writing.
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?
The tool has 3 parameters, an output schema, and no annotations. The description is too minimal to provide a complete picture—it lacks details on what 'optimal' means, how the analysis works, expected outputs, or any constraints. Given the complexity, this is under-specified.
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?
The schema description coverage is 0%. The description only mentions 'message' indirectly ('Analyze a message'), but provides no explanation of the 'strategy' or 'conversation_history' parameters. Since the description does not compensate for the lack of schema documentation, parameter semantics are poorly conveyed.
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 clearly states the tool's function: 'Analyze a message and get optimal LLM sampling parameters from AutoTune.' This is a specific verb+resource combination. However, it does not explicitly differentiate from sibling tools (e.g., single_chat, list_models), though the unique 'AutoTune' reference helps.
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 usage context is provided. The description does not indicate when to use this tool versus alternatives, nor does it mention any prerequisite conditions or exclusions. It simply states what the tool does without guidance on selection.
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 full burden of behavioral disclosure. It states the tool does not transform text, which is a useful trait, but does not disclose what detection returns, whether there are side effects, or any permission requirements. This is minimal.
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 filler words. It front-loads the core action and the distinguishing constraint, but could arguably include more detail without losing conciseness.
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?
Although an output schema exists and return values need not be described, the description lacks essential context about parameters and usage guidance. Given the tool's simple nature, it should provide more detail, especially since annotations are absent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description does not add any meaning for the parameters 'text' or 'custom_triggers'. The tool name and parameter titles are the only hints, so the description fails to compensate for the lack of parameter explanations.
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 the specific verb 'detect' with the object 'trigger words' and adds the distinguishing constraint 'without transforming them'. This clearly separates it from sibling tools like parseltongue_encode and transform_text, and communicates the core action and scope.
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?
The description implies use for detection-only scenarios but provides no explicit guidance on when to choose this tool over alternatives such as parseltongue_encode or transform_text. There is no mention of when not to use it or prerequisite conditions.
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 must carry full behavioral disclosure. It implies a read-only operation ('show') but does not disclose error conditions, performance implications, or what happens when no dataset is in memory. Minimal behavioral detail beyond the obvious.
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, concise sentence with no redundant words. It is front-loaded and every word contributes meaning, exemplifying strong conciseness.
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?
The tool is simple (0 params, output schema present), so the description need not explain return values. However, it lacks essential context about what 'in-memory dataset' refers to and how this tool relates to sibling tools like 'research_stats' or 'export_dataset', leaving a gap in overall completeness.
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 an empty schema, so the description does not need to explain parameter semantics. The baseline of 4 applies, and there is no additional information required.
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 clearly identifies the action ('Show') and the resource ('in-memory dataset statistics'), making the tool's purpose understandable. However, it does not distinguish it from sibling tools like 'research_stats' or 'export_dataset', and 'in-memory dataset' is somewhat ambiguous without parameter context.
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 provided on when to use this tool versus alternatives, nor any prerequisites such as requiring a dataset to be loaded. The description is purely declarative and offers no contextual usage direction.
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 carries the full burden of behavioral disclosure. It implies a read-only operation ('Return info') but does not explicitly state that it causes no side effects, nor does it mention authentication requirements, response structure, or any other behavioral traits. The word 'info' weakly suggests non-mutating behavior, but this is insufficient.
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 action ('Return') and directly states the core output. It contains no filler or redundant information. Every word 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 zero-parameter informational tool with an output schema present, the description is largely sufficient: it names the two key outputs ('schema' and 'repository info'). However, 'repository info' is slightly vague, and the lack of any usage guidance leaves some contextual gap, but the presence of an output schema reduces the need to explain return values.
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 the baseline of 4 applies. The description need not elaborate on parameters because none exist; the empty schema fully documents parameter expectations. The description simply affirms there is nothing to configure.
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 uses a specific verb ('Return') and identifies the resource ('research dataset schema and repository info'), making the tool's purpose clear. It does not explicitly distinguish itself from sibling tools like research_query or dataset_stats, but the focus on schema and repository info implies a metacognitive/informational role rather than data retrieval or analysis.
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?
The description provides no guidance on when to use this tool versus alternatives. There is no mention of prerequisites, exclusions, or preferred scenarios. The sibling tool list implies possible overlap with dataset_stats and research_stats, but the description does not address these distinctions.
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, and the description only says 'Return', implying a read operation but without disclosing side effects, authentication requirements, or response format. It also does not clarify what 'batches' refers to or the scope of aggregation.
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, front-loaded with the primary action, and contains no unnecessary words. It is concise and well-structured.
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 tool with an output schema available, the description is largely sufficient. However, it does not explain what 'aggregate stats' includes or define 'published HuggingFace batches', which could be ambiguous. It also lacks usage context, though this is mitigated by the simplicity of the 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, and schema coverage is 100%. The description adds no parameter-level detail, but with no parameters to document, the baseline of 4 is appropriate.
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 'Return aggregate stats across published HuggingFace batches' with a specific verb and resource. It clearly indicates what the tool does, but it does not explicitly differentiate from sibling tools like dataset_stats or research_query.
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 information is provided about when to use this tool versus alternatives. The description only says what it does, with no context on when it should be preferred over sibling tools such as dataset_stats or research_info.
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, the description must disclose behavioral traits like safety, permissions, or side effects. It only states the function, not whether the operation is read-only, if any size limits apply, or if it is suitable for large datasets. The agent is left without critical transparency information.
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, direct sentence with no fluff. It conveys the essential information efficiently and is perfectly scoped for the tool's simplicity.
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 has only one optional parameter and an output schema, the description is mostly sufficient. However, it omits any note about what 'the dataset' refers to or potential size/performance implications, which would be especially relevant for an export operation. Still, for a one-param tool, it is relatively complete.
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 schema only defines a 'format' string with a default. The description adds meaning by specifying the allowed values ('JSON or JSONL'), which directly clarifies the parameter. This compensates for the 0% schema description coverage.
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 (Export) and the resource (the dataset) with specific output formats (JSON or JSONL). It is distinct from sibling tools that handle stats, chats, or research, making its purpose unambiguous.
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 provided on when to use this tool versus alternatives. It is implied that one should use this when needing to export data, but there is no explicit context or exclusions, leaving the agent without decision-support.
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 carries the full burden. It does not disclose whether the operation is read-only, requires authentication, or has any limitations. The description only lists return content without addressing side effects or operational context.
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 concise sentence, eight words, with no fluff. It is directly front-loaded with the action and resource.
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 tool with an output schema, the description is sufficiently complete. It clearly states what the tool returns. However, it could mention whether authentication is needed or any caveats, but the simplicity and output schema mitigate this.
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 the schema fully covers them. The description adds useful semantics by specifying what the output includes (API info, endpoints, tier defaults), which goes beyond the empty schema.
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 clear verb ('Return') and a specific resource ('G0DM0D3 API info, endpoints, and tier defaults'). It distinguishes from sibling tools like health_check (which likely checks status) and get_tier (which likely retrieves a specific tier).
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 provided about when to use this tool versus alternatives. The description only states what it returns, with no mention of exclusions, prerequisites, or scenarios where another tool would be more appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description only says 'Check connectivity' without disclosing whether this is a read-only, safe operation, whether it requires authentication, or how failures are reported. Since no annotations are provided, the description carries the full burden and fails to provide meaningful behavioral context.
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, focused sentence that conveys the tool's purpose without unnecessary words, front-loading the action and resource.
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?
Given the tool's simplicity (no parameters) and the presence of an output schema, the description is minimally sufficient. However, it lacks any mention of error handling or usage context, leaving the agent uncertain about what a failed connectivity check implies.
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?
With zero parameters, the description does not need to explain parameter semantics. The baseline of 4 applies because no parameter information is missing.
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 clear action verb 'Check' and a specific resource 'connectivity to the G0DM0D3 API', making the tool's purpose unambiguous and distinct from sibling tools focused on models, chat, or data operations.
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?
No explicit guidance is given for when to use this tool versus alternatives. The purpose of a health check is self-evident, but the description does not state typical use cases like verifying API readiness before other calls or any prerequisites.
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 implies a read-only operation via 'Show' and suggests user-specific data with 'your current', but does not explicitly state safety, authentication needs, or side effects. Some transparency is present but limited.
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 concise sentence, front-loaded with the action and resource. Every word contributes meaning with zero 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?
This is a simple tool with an output schema and no parameters. The description sufficiently covers what the tool returns. It lacks an explicit note about authentication or usage context, but given the simplicity, it is mostly complete.
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 the schema provides no param info. The description adds value by enumerating the output content (tier, limits, features), which helps set expectations. Baseline 4 for zero-param tools 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 uses a specific verb ('Show') and identifies the resource ('your current G0DM0D3 API tier, limits, and features'). It clearly distinguishes from sibling tools like server_info or list_models by focusing on tier-specific details.
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 provided on when to use this tool versus alternatives. The description only states what it does without mentioning prerequisites, exclusions, or context for selection.
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 carries full burden. It only states the action and scope; it does not disclose whether any setup is required, whether the list is exhaustive, or any details about the returned data format. This is minimal for a read-only 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?
A single, tightly written sentence with no filler. The main action and distinguishing scope are front-loaded.
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 (zero parameters) and the presence of an output schema, the description is complete enough for an agent to know what to expect. It could add a note about the response format, but the output schema likely covers that.
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 already covers 100% of the (empty) parameter space. Per guidelines, a baseline of 4 applies; no additional parameter semantics 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 resource ('available models'), and explicitly distinguishes the scope by mentioning virtual ULTRAPLINIAN/CONSORTIUM models, differentiating it from sibling tools like get_tier or health_check.
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 to use it (when needing the list of available models) but provides no explicit alternatives, exclusions, or contrasts with sibling tools. It is clear enough for a simple zero-parameter listing 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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