llm-browser-multicast-mcp

by isaka1022

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Latest release: v0.1.0
Disambiguation5/5
Each tool serves a clearly distinct purpose: three different discussion formats (council, roundtable, debate) are explicitly differentiated by their mechanics, ask_model vs ask_models distinguishes single vs. parallel queries, and list_models and deep_research are unique. There is no meaningful overlap that would cause an agent to misselect.
Naming Consistency4/5
Most tools follow a verb_noun pattern (list_models, ask_models, ask_model) but the three discussion tools use a noun_verb pattern (council_discuss, roundtable_discuss, debate_discuss) and deep_research breaks the convention entirely. Despite the mixing, the names are readable and understandable.
Tool Count5/5
With 7 tools, the set is well-scoped for a multi-LLM discussion and query server. Each tool covers a distinct mode of interaction, and there is no bloat or trivial filler.
Completeness5/5
The tool surface completely covers the domain of orchestrating multi-LLM discussions: listing available models, querying one or many models, running three distinct discussion formats, and performing deep research. There are no obvious missing operations that would leave agents stuck.
Average 4/5 across 7 of 7 tools scored.
See the Tool Scores section below for per-tool breakdowns.
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This repository is licensed under MIT License.
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

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 does disclose core process behaviors—turns, building on previous responses—but omits any side effects, output format, or stopping behavior. It adds some behavioral context (conversational style) but remains thin for an orchestration 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?
Two concise sentences, front-loaded with the action and scope. Every clause adds meaning—run action, turn-taking behavior, building on responses, conversational style—and there is no filler or redundancy.
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 has three parameters and no output schema, so the description should at least indicate the return/discussion flow. It is clear about the conversational process but does not state the output contents (e.g., final transcript, per-round responses). It is adequate for basic selection but incomplete for an agent wanting to interpret the result.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema covers 100% of parameters with descriptions, so the baseline is 3; the description adds no extra parameter-level detail beyond loosely mapping 'models take turns' to the rounds/models concept. Defaults and constraints are already in the schema, so the description does not need to compensate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description opens with 'Run a roundtable discussion'—a specific verb and resource—and clarifies the mode: 'models take turns responding, building on previous responses.' The final clause 'More natural, conversational debate style' differentiates it from stricter debate/council formats among siblings.
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 use for natural, turn-taking debate-style discussions ('More natural, conversational debate style'), suggesting a contrast with more formal siblings like debate_discuss and council_discuss, but it never explicitly states when to prefer this tool or names alternatives. No exclusions 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.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries a full burden. It transparently discloses the behavioral flow: all models answer in parallel, peer-review anonymously, and a chairman synthesizes the best answer. This sets accurate expectations, though it does not cover return format or failure modes.
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 and then explains the workflow. It contains no fluff or redundant information.
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's complexity is moderate, and there is no output schema. The description effectively explains the process but does not explicitly state what the return value is (e.g., only the chairman's synthesis or all contributions) nor mention configuration prerequisites. These gaps make it not fully complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, with all three parameters already described in the input schema. The tool description adds no additional parameter-specific semantics, so the baseline 3 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's purpose with a specific verb and resource: 'Run a council discussion' and explains the three-step process (parallel answers, anonymous peer-review, chairman synthesis). This distinguishes it from sibling tools like debate or roundtable discussions.
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 the tool (when a multi-perspective, synthesized answer is desired) but does not explicitly mention when to choose this over alternatives like roundtable_discuss or debate_discuss. There is no exclusionary language or reference to sibling tools.
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 burden of behavioral disclosure. 'List' implies a read-only operation, but it doesn't disclose any potential side effects, output format, or edge cases (e.g., behavior when no providers are configured). It's 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?
A single, well-structured sentence that immediately states purpose. No wasted words or redundant information.
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, but with no output schema, the description should mention what the returned list contains (e.g., model IDs, names). It doesn't, leaving a minor gap in completeness for a low-complexity 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 the baseline is 4. The description correctly focuses on the action, and the schema covers the absence of parameters fully.
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 'List' with a clear resource ('all available models from configured providers'). It clearly distinguishes from sibling tools like council_discuss and ask_model, which involve discussions or queries.
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 usage for discovering available models but provides no explicit when-to-use guidance or alternatives. It's self-evident for a simple listing tool but lacks explicit context or exclusions.
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 of behavioral disclosure. It adds useful context about facilitator-style orchestration and user intervention between rounds, but does not disclose return format, rate limits, or required permissions. It is minimally adequate but could say more about what happens after the call.
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 ('Ask a single model a question') followed by a concise usage scenario. Every word earns its place, with no fluff or repetition.
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 relatively simple tool with no output schema and 100% schema parameter coverage, the description sufficiently captures the purpose and use case. It could have mentioned the return value or a note about session persistence, but this is not critical given the schema clarity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline is 3. The description does not add any additional parameter semantics beyond the schema, but the schema already documents model, prompt, context, and session_id clearly. No compensation 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 clearly specifies 'Ask a single model a question' with a specific verb and resource, distinguishing it from sibling tools like ask_models (plural) and discussion-based tools (council_discuss, roundtable_discuss, debate_discuss). The scope 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 provides clear context: 'Use this for interactive, facilitator-style discussions where Claude Code orchestrates the conversation and the user can intervene between rounds.' It does not explicitly state when not to use it or name alternatives, but the context implies a single-model interactive use case.
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, so the description carries full burden. It discloses parallel execution and response aggregation, but lacks error handling or partial-failure behavior, which would be useful for an agent to anticipate.
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 sentences, front-loaded with the core action, and includes return behavior and use case. 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 simple query tool, the description covers purpose, use case, and core behavior. Missing explicit error/partial-failure handling and output structure, but schema covers params. Adequately complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema covers all parameters with descriptions (100% coverage). The tool description adds little beyond what the schema already provides, so baseline 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?
Clear verb 'Ask' + resource 'multiple models' + 'in parallel'. Distinguishes from ask_model (singular) and positions it as a parallel query tool, not a discussion tool.
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?
Explicit use case: 'Use for gathering diverse opinions that Claude Code can then synthesize with the user.' This gives clear context but doesn't explicitly mention alternatives or when not to use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses the full behavioral workflow (state positions, discuss in rounds, restate, synthesize), which is valuable beyond the simple name. However, it does not specify the return format or what the tool produces beyond the chairman's synthesis, and no annotations exist to cover these aspects.
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 core purpose and lists the process steps efficiently, with no filler or redundant information.
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 description covers the tool's workflow and gives clues about the final output (chairman's synthesis), but lacks an explicit statement of the return value or result structure. Since no output schema exists, this omission leaves a minor gap, though the process is otherwise well explained.
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 fully describes all three parameters (models, rounds, question) with defaults and constraints. The tool description adds process context (how rounds and positions relate) but no additional semantic or syntactic details beyond the 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 clearly states the tool 'Run a structured debate' and details the multi-stage process (positions, rounds, final positions, chairman synthesis), which distinctly identifies it from sibling discussion 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?
No explicit usage guidance is provided; no alternatives or conditions are mentioned. Usage is only implied by the description's focus on structured debates, so it falls short of explicit guidelines.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full transparency burden. It discloses key behavioral traits: the 5-15 minute duration and the significant side effect that it 'blocks other ChatGPT operations during research.' It also mentions the return type (comprehensive results with sources). While it doesn't cover potential errors or cancellation, the most critical behaviors are surfaced.
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: the first states the purpose and scope, the second adds a critical warning and expected output. Every sentence provides necessary information, with no fluff or repetition. It is front-loaded with the primary action and concise enough to be parsed quickly.
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 no output schema, the description adequately covers what the agent can expect (comprehensive results with sources) and the major operational constraint (blocks other operations). It could go further by specifying whether the blocking is global or per-resource, or how the timeout parameter should be interpreted relative to the 5-15 minute estimate, but these are minor gaps. Overall, the description is complete enough for correct selection and invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so both query and timeout_minutes are documented in the input schema. The description adds no additional meaning about these parameters; it doesn't explain how the timeout interacts with the stated 5-15 minute research time. The description does not need to compensate since the schema is complete, so a 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 clearly states the action ('Run ChatGPT Deep Research') and the resource ('on a query'), then elaborates with 'Performs in-depth web research' and 'Returns comprehensive results with sources.' This specific verb+resource combination distinguishes it from sibling tools like council_discuss or ask_model, which are discussion/query tools rather than research tools.
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: for in-depth web research via ChatGPT's Deep Research mode. It implies it is best for comprehensive queries rather than quick questions, but it does not explicitly name alternatives or state when not to use it. The lack of explicit exclusions prevents a 5, but the context is unambiguous.
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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