HuangtingFlux — Huangting Protocol MCP Server

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, description carries full burden and successfully discloses: (1) the refinement algorithm used, (2) token savings computation logic, (3) side effect of appending a 'mandatory performance report table', (4) data anonymization for agent_id, and (5) output handling requirements (MUST present returned content). Could be elevated by mentioning error handling or idempotency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Four sentences with zero waste. Front-loaded mandatory warning [MANDATORY — CALL LAST], followed by trigger condition, server operation enumeration (1)(2)(3), and output presentation requirement. Information density is high but readable.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Adequately explains the 3-phase protocol workflow (Huangting Protocol SOP), integration points with start_task (context_id, baseline), server-side processing details, and required user-facing output handling. Lacking only explicit error scenarios or retry guidance for a complex mutate-and-report operation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% (baseline 3). Description adds narrative value by mapping final_content to 'draft final answer', context_id to 'returned by start_task', and baseline_tokens to values from 'start_task response', providing workflow context that pure schema descriptions lack.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states this tool finalizes tasks using the 'Void-Refined Output algorithm' and generates performance reports, distinguishing it from siblings start_task (initiation) and report_step_result (intermediate reporting) by explicitly identifying itself as 'Phase 3' and 'CALL LAST'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Extremely explicit temporal guidance: '[MANDATORY — CALL LAST]' and 'Call this when your task is COMPLETE'. It clearly positions this as the final workflow step, implying siblings are for earlier phases without needing to enumerate them.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden. It discloses 'real-time' freshness and enumerates specific output fields (tokens saved, agent count, etc.), but lacks operational details like rate limits, caching behavior, or payload size warnings.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Single sentence with high information density. Front-loaded purpose ('Get real-time global statistics') followed by colon-delimited list of specific metrics. No redundant or wasteful text.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple parameterless tool with no output schema, the description adequately compensates by enumerating the expected return values (statistics fields). It satisfies the minimal needs, though a note on frequency of updates or data retention would enhance completeness.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has zero parameters, establishing a baseline of 4. The description correctly omits parameter discussion entirely, which is appropriate given the empty schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb ('Get') and resource ('real-time global statistics of the HuangtingFlux optimization network'), and lists specific metrics returned. However, it does not explicitly differentiate from siblings (finalize_and_report, report_step_result, start_task), though the distinct resource makes this implicit.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explains what the tool returns but provides no guidance on when to invoke it (e.g., monitoring scenarios, during task execution vs. idle time) or prerequisites. No comparison to sibling workflow tools is offered.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, description carries full burden. It discloses side effects (broadcasts to real-time network dashboard) and strategic purpose (Ego-Chain Summarization). However, it lacks safety information (idempotency, failure handling, reversibility) that would fully equip an agent to use this safely.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with mandatory flag and workflow position (Phase 2). Information-dense without excessive length. Minor deduction for domain-specific jargon ('Huangting Protocol,' 'Ego-Chain') that may not aid all agents, though it provides taxonomy for context.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Appropriately complete for a 4-parameter intermediate-step tool with no output schema. Explains the 'why' (context window management) and workflow integration sufficiently. Could be strengthened by explicitly positioning against finalize_and_report in the workflow description.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, establishing baseline 3. The description adds minimal semantic value beyond the schema since the schema already documents the start_task linkage for context_id and field purposes. No additional syntax hints or format examples provided.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Description uses specific verbs (records, broadcasts) and identifies the resource (step results, token costs). It clearly distinguishes from siblings by emphasizing 'after EACH intermediate reasoning step' (contrasting with finalize_and_report) and explicitly links to start_task in the workflow sequence.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly flags usage as '[MANDATORY]' and states 'Call this after EACH intermediate reasoning step,' establishing clear timing. Mentions prerequisite (context_id from start_task). Lacks explicit 'when not to use' or direct reference to finalize_and_report as the alternative for final reporting.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full disclosure burden. It effectively explains the compression effect (30-60% token reduction), state creation (unique context_id), and return structure (three-stage plan). It mentions this is part of the 'Huangting Protocol SOP.' Minor gap:does not clarify if this is idempotent,rate-limited, or persistent across sessions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

appropriately front-loaded with '[MANDATORY — CALL FIRST]' but contains redundancy ('You MUST... You MUST... This is NOT optional'). The repetition enforces protocol compliance but reduces economy. Structure is logical: mandate → mechanism → output obligations → universality.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Comprehensive for a workflow initialization tool: describes the three return values (core_instruction, context_id, optimization plan) and their downstream usage despite lacking an output schema. Explicitly links to sibling tools (report_step_result, finalize_and_report). Missing only error handling or state persistence details.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with detailed enum descriptions for task_type (including step counts and multipliers) and model. The description reinforces the purpose of task_description ('verbose task description') but does not add parameter syntax or mappings beyond the schema. Baseline 3 is appropriate given schema completeness.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description explicitly states the tool compresses task descriptions into Core Instructions, creates unique context_ids, and returns three-stage optimization plans. It clearly positions itself as 'Phase 1' and distinguishes from siblings by naming report_step_result and finalize_and_report as subsequent steps that require the generated context_id.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Excellent temporal guidance: 'Call this at the START of ANY complex, multi-step task before doing anything else.' Explicit obligation markers ('MUST', 'NOT optional') clarify this is mandatory. It specifies exact usage of outputs (core_instruction as guiding principle, context_id for specific siblings) and explicitly scopes applicability to ALL task types.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.