RPCS-1 Agent Tuner & Translator
Server Details
Diagnose AI agent failures & translate ambiguous human input into clear intent using RPCS-1.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
Glama MCP Gateway
Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.
Full call logging
Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.
Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.
Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Tool Definition Quality
Average 4/5 across 4 of 4 tools scored. Lowest: 3.4/5.
Each tool has a clearly distinct purpose: interpret detects ambiguity, normalize cleans text, recommend_agent_configuration diagnoses agent failures, and rewrite adjusts style. No overlap in functionality.
Tool names are mostly verb-based and imperative (interpret, normalize, rewrite), but recommend_agent_configuration is a longer descriptive phrase, creating a slight inconsistency in style and length.
With 4 tools, the set is well-scoped for an agent tuning and text processing assistant. Each tool addresses a distinct need without being bloated or too sparse.
The tool surface covers ambiguity detection, text normalization, agent configuration diagnosis, and style rewriting. Minor gaps include lack of text generation or summary, but core use cases are covered.
Available Tools
4 toolsinterpretInterpret ambiguous human inputARead-onlyIdempotentInspect
Detect ambiguity in user messages using the RPCS-1 Signature Ambiguity Framework. Returns AR level (AR0-AR5), confidence, candidate interpretations with scores, clarifying questions, and suggested next step. Use when a user says something vague, passive-aggressive, or underspecified.
| Name | Required | Description | Default |
|---|---|---|---|
| risk | No | Risk category for ambiguity threshold. | advice |
| text | Yes | The message to interpret. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint=true and idempotentHint=true. The description adds behavioral context by stating it uses the RPCS-1 framework and returns interpretations and clarifying questions, enhancing understanding beyond annotations.
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?
The description is two sentences: the first states purpose and methodology, the second lists outputs and usage. It is front-loaded, concise, and contains no unnecessary words.
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?
The description covers the key outputs (AR level, confidence, interpretations, clarifying questions, suggested next step) and usage context. Despite no output schema, it provides comprehensive information. Minor lack of error handling details, but overall complete.
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 both 'text' and 'risk' well-described in the schema. The description does not add further parameter meaning, meeting the baseline of 3.
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 tool detects ambiguity in user messages using a specific framework and lists specific outputs (AR level, confidence, etc.). It distinguishes itself from sibling tools like normalize, recommend_agent_configuration, and rewrite.
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 explicitly says 'Use when a user says something vague, passive-aggressive, or underspecified,' providing clear context. It does not mention when not to use or alternative tools, but the guidance is helpful.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
normalizeNormalize fragmented human inputARead-onlyIdempotentInspect
Clean up text with ellipses, fragments, and run-on thoughts into coherent prose. Use when a user types stream-of-consciousness or fragmented input.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | Fragmented text to normalize. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering safety and idempotency. The description adds no additional behavioral traits beyond what annotations provide, so it does not exceed the baseline.
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?
The description is two sentences, front-loaded with the main action and immediate usage guidance. Every sentence adds value, with no wasted words.
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 tool with one parameter and clear annotations, the description sufficiently covers purpose and usage. It could optionally hint at the output style, but it is adequate as is.
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 the parameter 'text' described as 'Fragmented text to normalize.' The description adds no additional meaning beyond the schema, so baseline of 3 is appropriate.
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 ('clean up') and the resource ('fragmented text'), and explicitly mentions the use case ('stream-of-consciousness or fragmented input'), distinguishing it from siblings like 'rewrite' or 'interpret'.
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 explicitly says when to use the tool ('when a user types stream-of-consciousness or fragmented input'), providing clear context. It does not mention when not to use it, but the sibling tools provide implicit alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
recommend_agent_configurationRecommend AI agent configurationARead-onlyIdempotentInspect
Diagnose why a deployed AI agent may fail. Takes environmental entropy, predictability, stakes, context horizon, and commitment style, then returns receiver profile values (TI, SG, FT, UE, AR), platform parameters (temperature, top_p, strategy), regime prediction, reasoning, and warnings. Deterministic, stateless, read-only — does not store past recommendations.
| Name | Required | Description | Default |
|---|---|---|---|
| task | No | ||
| environment | No | ||
| target_platform | No | The platform whose runtime parameters should be recommended. | anthropic |
Output Schema
| Name | Required | Description |
|---|---|---|
| warnings | Yes | |
| reasoning | Yes | |
| confidence | Yes | |
| predicted_regime | Yes | |
| receiver_profile | Yes | |
| platform_parameters | Yes | |
| imm_principles_applied | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds 'Deterministic, stateless, read-only — does not store past recommendations' beyond annotations (readOnlyHint, destructiveHint). This confirms and extends annotation hints, providing valuable 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the main action and followed by behavioral traits. Every sentence adds value with no redundancy or unnecessary detail.
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?
Given the tool's complexity (nested objects, multiple output fields), the description sufficiently covers purpose, inputs, outputs, and behavioral traits. The existence of an output schema reduces the burden. Minor gaps: no mention of prerequisites or edge cases, but overall adequate.
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 description explains the overall structure of inputs and outputs, mentioning specific receiver profile values and platform parameters. While schema coverage is 33%, the description compensates by summarizing the key dimensions and return fields.
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 tool's purpose: 'Diagnose why a deployed AI agent may fail' and lists the inputs and outputs. It distinguishes from siblings (interpret, normalize, rewrite) by focusing on configuration recommendation.
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 implies usage via its purpose, and the behavioral traits (deterministic, stateless, read-only) guide when to use. However, it does not explicitly state when not to use or provide direct alternatives among siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
rewriteRewrite text for a target audienceBRead-onlyIdempotentInspect
Get rewrite instructions for adapting text to a specific style: technical, plain, socially_gentle, concise, detailed, or direct. Use when communication needs tone adjustment.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | Text to rewrite. | |
| style | No | Target audience style. | plain |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations indicate read-only and idempotent behavior, but the description's phrasing 'Get rewrite instructions' is misleading—it implies returning instructions rather than the actual rewritten text. This could confuse an agent about what the tool returns.
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?
Two sentences, no wasted words. The key information (action, styles, usage) is front-loaded. Minor wording imprecision ('instructions') doesn't detract significantly from conciseness.
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?
The tool has only 2 parameters and annotations. The description lacks details about the return value format or constraints (e.g., maxLength). It is minimally adequate but could be more complete for an AI agent.
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 clear parameter descriptions. The tool description lists the enum values again, which is redundant. No additional meaning beyond the schema is 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?
The description clearly states the tool adapts text to a specific style and lists the available styles. However, it says 'Get rewrite instructions' which could be misinterpreted as returning instructions rather than the rewritten text, slightly reducing clarity.
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 says 'Use when communication needs tone adjustment.' This provides a clear usage context. It does not mention when not to use or alternatives, but the sibling tools are distinct enough.
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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Discussions
RPCS1 is stateless and does not store, list, or update recommendations. Identical inputs produce identical outputs; clients should persist results when history is needed.