Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Sutra help me design a debate agent for analyzing climate policy"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
mcp-name: io.github.4rgon4ut/sutra
The MCP Context Engineering Engine
Sutra is a Model Context Protocol (MCP) server that transforms how LLMs handle reasoning, memory, and orchestration. It provides a "Standard Library" of cognitive tools (Thinking Models), memory structures (Cells), and multi-agent patterns (Organs).
uv (Recommended)uv tool install context-engineering-mcppippip install context-engineering-mcpRelated MCP server: MCP Think Tank
Select your agent below and copy-paste the config.
Add to claude_desktop_config.json:
{
"mcpServers": {
"sutra": {
"command": "uv",
"args": ["tool", "run", "context-engineering-mcp"]
}
}
}Run this in your terminal:
claude mcp add sutra uv tool run context-engineering-mcpRun aider with the mcp flag:
aider --mcp "uv tool run context-engineering-mcp"Or add to .aider.conf.yml:
mcp: ["uv tool run context-engineering-mcp"]Add to ~/.gemini/settings.json:
{
"mcpServers": {
"sutra": {
"command": "uv",
"args": ["tool", "run", "context-engineering-mcp"]
}
}
}In MCP settings, add a new server:
Name: Sutra
Type: command
Command: uv tool run context-engineering-mcp
Add to your config (TOML):
[mcp_servers.sutra]
command = "uv"
args = ["tool", "run", "context-engineering-mcp"]Sutra automatically analyzes your request to decide the best strategy:
YOLO Mode: For immediate tasks ("Fix this bug"), it routes to specific cognitive tools.
Constructor Mode: For system design ("Build a bot"), it routes to the Architect.
Generates blueprints for custom agents, combining:
Thinking Models: understand_question, verify_logic, backtracking, symbolic_abstract.
Memory Cells: key_value (State), windowed (Short-term), episodic (Long-term).
Organs: debate_council (Multi-perspective), research_synthesis (Deep Dive).
A manual discovery tool (get_technique_guide) that lets you or the agent browse the full catalog of Context Engineering techniques.
Requirements: Python 3.10+, uv (optional but recommended).
Clone the repo:
git clone https://github.com/4rgon4ut/sutra.git
cd sutraInstall dependencies:
uv sync --all-extras
# OR
pip install -e ".[dev]"Run tests:
pytestMIT
Analyzes a task to recommend the most efficient tool (The Router).
Args:
task_description: The user's prompt or task.
| Name | Required | Description | Default |
|---|---|---|---|
| task_description | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations present, so description must disclose behavior. It does not state if the tool is read-only, has side effects, or any rate limits. Only says 'analyzes and recommends', which implies but does not confirm safety.
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?
Very short, but missing critical information like output format. Not concise in a helpful way; it is under-specified.
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?
With 0% schema coverage, no output schema, and no annotations, the description lacks context. Does not explain return value or how the recommendation is presented.
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 description coverage is 0%. The description only repeats the parameter name and type without adding meaning, format, or examples. No value over 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?
Description states it analyzes a task and recommends a tool, but 'The Router' is ambiguous and not defined. It distinguishes from siblings in that it's a meta-tool, but specificity is low.
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?
No guidance on when to use this tool versus alternatives. Does not mention prerequisites or context for optimal use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Produce a recursive backtracking scaffold for error correction.
Args:
objective: Overall goal to satisfy.
failed_step: The step or subgoal that failed.
trace: Optional reasoning trace leading to the failure.
constraints: Guardrails or requirements to respect.
Returns:
Structured prompt that rewinds to last stable state, explores
alternatives, and proposes a patched plan.
| Name | Required | Description | Default |
|---|---|---|---|
| objective | Yes | ||
| failed_step | Yes | ||
| trace | No | ||
| constraints | No |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It explains the tool rewinds to the last stable state, explores alternatives, and produces a patched plan. It does not mention side effects or permissions, but for a prompt generation tool, this is adequate.
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 a well-structured docstring with Args and Returns sections. It is concise, using minimal but complete sentences. Minor fluff could be trimmed, but it is effective.
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 lack of annotations, the description covers the tool's purpose, inputs, and output sufficiently. It does not mention idempotency or error handling, but for a scaffold generator, this is reasonable.
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 description coverage is 0%, so the description must compensate fully. It does so by explaining each parameter: objective as overall goal, failed_step as the step that failed, trace as optional reasoning trace, constraints as guardrails. This adds meaningful context beyond the 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 it produces a recursive backtracking scaffold for error correction, with a specific verb and resource. It is distinct from sibling tools like 'analyze_task_complexity' or 'design_context_architecture' which focus on analysis and design, not error correction.
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 when an error or failure occurs, with parameters for the failed step and objective. However, it does not explicitly state when not to use or suggest alternative tools, which would improve guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Architects a custom context system based on a high-level goal (The Architect).
Returns a blueprint of Sutra components (Molecules, Cells, Organs, Thinking Models).
Use this when the user wants to build a persistent agent or complex workflow
rather than solving a single immediate task.
Args:
goal: The user's objective (e.g., "Build a writing assistant that learns my style").
constraints: Optional limits (e.g., "Must be lightweight").
| Name | Required | Description | Default |
|---|---|---|---|
| goal | Yes | ||
| constraints | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided; description does not disclose side effects, authentication needs, or what 'persistent' entails. The term 'blueprint' is vague without output schema.
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?
Efficient use of three short paragraphs; front-loaded with main action and usage. No redundancy.
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?
Lacks details on return structure, side effects, and prerequisites. Without output schema, more description needed on the blueprint format and components.
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?
Adds meaning to parameters with descriptions and examples (e.g., 'goal: The user's objective'), compensating for 0% schema coverage. However, no additional constraints or format details.
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 uses a specific verb 'Architects' and identifies the resource as a 'custom context system', with return output specified. It distinguishes from sibling tools that retrieve specific components.
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 states when to use: 'when the user wants to build a persistent agent or complex workflow rather than solving a single immediate task', but does not list alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Returns a cell protocol template describing memory behaviors.
Args:
name: Identifier of the cell protocol (key_value, windowed, episodic).
| Name | Required | Description | Default |
|---|---|---|---|
| name | No | cell.protocol.key_value |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description only indicates it returns a template, implying a read-only operation. It does not disclose potential side effects, permissions, or limitations, but the tool appears simple and safe.
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 short and to the point, with a clear sentence followed by parameter documentation. It is well-structured and easy to parse.
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 low complexity (one parameter, output schema exists), the description sufficiently covers its purpose and inputs. It is adequate for an agent to invoke it correctly.
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 documents the parameter 'name' with its allowed values ('key_value, windowed, episodic'), adding meaning beyond the raw schema which has 0% description coverage.
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 action (returns), the resource (cell protocol template), and the purpose (describing memory behaviors). It distinguishes the tool from siblings by specifying its focus on cell protocols for memory.
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?
No guidance is provided on when to use this tool versus alternatives like get_molecular_template or get_protocol_shell. It lacks context for selection among sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Returns the Python function for creating molecular contexts (Module 02). Use this to programmatically construct few-shot prompts.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
| result | Yes |
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 only mentions the return value (a Python function) but does not specify whether the operation is read-only, requires authentication, or has any side effects. This is minimal for a tool with no 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?
Two sentences, no fluff, front-loaded with the core action. Every word earns its place.
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 an output schema, so description need not detail return structure. It covers purpose and usage adequately, though it could elaborate on how to invoke the returned function.
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 no parameters, so the baseline is 4. The description adds value by explaining what the returned object does (creates molecular contexts), beyond 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 tool returns a Python function for creating molecular contexts, with a specific module reference (Module 02) and a distinct use case (programmatic few-shot prompt construction). This differentiates it from sibling tools like get_cell_protocol or get_organ.
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 this to programmatically construct few-shot prompts,' providing clear context for when to use the tool. However, it does not state when not to use it or mention alternatives, though sibling tool names imply other modules.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Returns an organ template for multi-agent orchestration (Layer 4).
Organs combine programs and cells into cohesive workflows for complex tasks
requiring multi-perspective analysis or collaborative reasoning.
Args:
name: Identifier of the organ ('debate_council' for multi-perspective debate).
| Name | Required | Description | Default |
|---|---|---|---|
| name | No | tool_master |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must cover behavioral traits. It does not disclose any side effects, return structure details, or potential constraints beyond the basic operation of returning a template. This is insufficient given the lack of 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 concise and front-loaded with the core purpose. It includes a brief docstring-style format with args section. No wasted sentences, though it could be slightly more efficient.
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 role in multi-agent orchestration and the existence of an output schema, the description does not explain what the organ template contains or how it should be used. It lacks completeness for a tool at this complexity level.
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 only parameter 'name' has a default and title in schema. The description adds an example ('debate_council') and explains its purpose as an organ identifier. With 0% schema coverage, this adds value but still lacks full semantic details.
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 the tool returns an organ template for multi-agent orchestration at Layer 4. It distinguishes from siblings by specifying the role (combines programs and cells) and complex tasks, which differentiates it from similar tools like get_cell_protocol or get_molecular_template.
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?
It provides context on when to use (complex tasks requiring multi-perspective analysis or collaborative reasoning) and gives an example argument ('debate_council'). However, it does not explicitly state when not to use or compare directly to sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Returns a functional pseudo-code prompt template (Module 07).
Args:
program_type: The type of program ('math', 'debate').
| Name | Required | Description | Default |
|---|---|---|---|
| program_type | No | math |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It states the output is a 'functional pseudo-code prompt template' but does not disclose behaviors such as side effects, authorization needs, or rate limits.
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 extremely concise, using two lines plus an argument list. Every word is necessary and front-loaded, efficiently conveying the tool's purpose.
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 has one parameter and an output schema (not shown here), the description is minimally adequate but lacks detail on what the returned template contains. More context would improve 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?
Despite 0% schema coverage, the description adds meaning by explaining the single parameter 'program_type' with its valid values ('math', 'debate'), which is not present in the 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 it returns a functional pseudo-code prompt template, with specific mention of Module 07. This makes the tool's purpose clear, though it does not explicitly differentiate from sibling tools.
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?
There is no guidance on when to use this tool versus alternatives. The description only lists the argument without providing context or criteria for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Returns a Protocol Shell. Can return a specific pre-defined template or a blank shell.
Args:
name: The name of the protocol (e.g., 'reasoning.systematic') OR a custom name.
intent: (Optional) The intent if creating a custom shell.
| Name | Required | Description | Default |
|---|---|---|---|
| name | No | MyProtocol | |
| intent | No |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears full responsibility. It states 'Returns a Protocol Shell' but does not disclose side effects, error behavior (e.g., if name not found), or whether the operation is read-only. The lack of behavioral details is a significant gap.
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 concise with a single sentence and a bullet list for arguments. It is well-structured and easy to parse. However, it could be slightly more concise by removing redundancy with the parameter definitions.
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 absence of annotations and low schema coverage, the description is insufficient. It does not explain what a Protocol Shell is, how it relates to siblings, or what the return value contains (despite an output schema, its content is not described). The agent lacks complete context to use the tool effectively.
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 0%, so the description adds meaning: 'name' can be a predefined template name or a custom name; 'intent' is optional for custom shells. However, it lacks format constraints or examples beyond 'reasoning.systematic', and does not clarify how the tool differentiates between a template name and a custom name.
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 'Returns a Protocol Shell' and distinguishes between returning a pre-defined template or a blank shell. However, it does not differentiate this tool from sibling tools like get_cell_protocol or get_technique_guide, which may also return protocol-related structures.
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?
No explicit guidance on when to use this tool versus alternatives. It mentions custom vs. pre-defined templates, but lacks context for prerequisites, typical use cases, or exclusions. The agent is left to infer usage from the parameter descriptions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Returns a guide to available Context Engineering techniques (The Librarian).
Use this to discover the best tool for a given task.
Args:
category: Filter by 'reasoning', 'workflow', 'code', 'project', or 'all'.
| Name | Required | Description | Default |
|---|---|---|---|
| category | No | all |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must convey behavior. It states it returns a guide and accepts a category filter, implying a read-only operation with no side effects. It does not disclose auth needs, rate limits, or other traits 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences plus an args list, with no redundant information. It is front-loaded with the primary purpose and efficiently explains the parameter.
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 that an output schema exists, the description does not need to detail return values. It covers the main purpose and filtering. It could mention that the guide is a list of techniques, but the schema likely provides structure.
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 one parameter with no enums or description. The description adds value by listing allowed category values ('reasoning', 'workflow', 'code', 'project', 'all'), providing meaning missing from the schema (schema coverage 0%).
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 that the tool returns a guide to Context Engineering techniques and is used to discover the best tool for a given task. The nickname 'The Librarian' reinforces the purpose. It is distinct from sibling tools like get_cell_protocol or analyze_task_complexity.
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 says 'Use this to discover the best tool for a given task,' providing clear usage context. However, it does not explicitly mention when not to use this tool or provide exclusions vs. siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Convert a concrete expression into abstract variables for reasoning.
Args:
expression: The raw text or equation to abstract.
mapping_hint: Optional guidance for token-to-symbol mapping.
goal: Optional downstream task (e.g., simplify, prove, generalize).
Returns:
Structured prompt that maps tokens to symbols, restates the problem
abstractly, and provides a reversible mapping table.
| Name | Required | Description | Default |
|---|---|---|---|
| expression | Yes | ||
| mapping_hint | No | ||
| goal | No |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description bears full responsibility for behavioral disclosure. It describes the output as a 'Structured prompt...' and mentions a reversible mapping table, but does not specify side effects (e.g., whether it modifies state), authentication needs, or error conditions. The description adds moderate transparency but lacks depth.
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 concise (under 100 words) and front-loaded with the purpose statement. It uses a structured docstring format for parameters and returns, which is efficient. However, the parameter descriptions are indented, making it slightly less scannable than a flat list, but still clear.
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 moderate complexity (3 parameters, 1 required, no enums, output schema present), the description adequately covers input, optional parameters, and output structure. It does not cover edge cases or error handling, but for a transformation tool, the provided information is largely 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?
The input schema has 0% description coverage, so the description must compensate. It provides meaningful explanations for each parameter: 'The raw text or equation to abstract' for expression, 'Optional guidance for token-to-symbol mapping' for mapping_hint, and 'Optional downstream task (e.g., simplify, prove, generalize)' for goal. This adds clarity beyond the schema's titles.
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 function: 'Convert a concrete expression into abstract variables for reasoning.' This uses a specific verb (convert) and resource (concrete expression) and outcome (abstract variables), distinguishing it from sibling tools that focus on retrieval (e.g., get_cell_protocol) or other reasoning tasks (e.g., backtracking).
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 provides no guidance on when to use this tool versus alternatives. There is no mention of context, prerequisites, or when not to use it. Sibling tools like analyze_task_complexity or backtracking are not referenced, so an agent has no basis for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Produce a protocol shell to decompose a user question.
Args:
question: The raw user ask to unpack.
context: Optional background knowledge or situational frame.
constraints: Explicit limits or success criteria.
Returns:
A structured prompt guiding the model to restate intent, surface
constraints, and prepare clarifying questions before acting.
| Name | Required | Description | Default |
|---|---|---|---|
| question | Yes | ||
| context | No | ||
| constraints | No |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses that the tool returns a structured prompt guiding model behavior, but does not specify side effects, safety, or performance characteristics. The read-only nature is implied but not stated.
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 structured with Args and Returns, making it easy to parse. However, the Args section repeats parameter names and is slightly verbose; a more streamlined version could remove redundant phrasing.
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 existence of an output schema, the description appropriately omits return details. It covers the three parameters and their roles. For a tool with moderate complexity (3 params, no enums), this is sufficient.
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?
Despite 0% schema description coverage, the description compensates well by explaining each parameter's purpose ('raw user ask', 'background knowledge', 'explicit limits'). This adds value beyond the bare schema, though it could be more precise about allowed formats.
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 produces a 'protocol shell' to decompose a user question, with a specific focus on intent restatement and constraint surfacing. While it differentiates from siblings like 'get_protocol_shell' by emphasizing decomposition, the phrase 'produce a protocol shell' could be confused with that sibling.
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?
No explicit guidance on when to use this tool versus alternatives (e.g., analyze_task_complexity). The description does not mention prerequisites, when-not-to-use, or alternative tools, leaving the agent to infer usage context from the name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Generate a verification protocol for a reasoning trace.
Args:
claim: The headline answer or assertion to validate.
reasoning_trace: The supporting chain-of-thought or proof steps.
constraints: Optional guardrails (requirements, risk limits).
Returns:
Structured prompt that audits assumptions, inference steps, and
evidence, then proposes patches for any defects.
| Name | Required | Description | Default |
|---|---|---|---|
| claim | Yes | ||
| reasoning_trace | Yes | ||
| constraints | No |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden for transparency. It discloses that the output is a 'structured prompt that audits assumptions, inference steps, and evidence, then proposes patches for any defects.' This explains the behavior well but lacks details on determinism, required permissions, or potential side effects (though none expected given the generative nature).
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 concise at approximately 5 lines, front-loads the purpose in the first sentence, and uses a clear docstring-style structure with 'Args' and 'Returns' sections. Every sentence adds value without redundancy or filler.
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, the description explains inputs and the nature of the output (a structured prompt for auditing and patching). Since an output schema exists, detailed return format is not required. However, it could include more context, such as expected input formats or limitations on trace length, but overall it is adequate for a straightforward generative tool.
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 0%, so the description must compensate. It explains each parameter: 'claim: The headline answer or assertion to validate,' 'reasoning_trace: The supporting chain-of-thought or proof steps,' and 'constraints: Optional guardrails (requirements, risk limits).' This adds significant meaning beyond the bare schema types, though it lacks format examples or precise constraints on input values.
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: 'Generate a verification protocol for a reasoning trace.' This specifies the verb 'generate,' the resource 'verification protocol,' and the domain 'reasoning trace.' It also distinguishes from sibling tools like 'analyze_task_complexity' or 'backtracking' by focusing on verification rather than analysis or search.
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 does not provide any guidance on when to use this tool versus alternatives. It lacks explicit context about prerequisites, recommended scenarios, or when not to use it. Sibling tools are not mentioned, so the agent must infer usage from the tool's purpose alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Each tool has a clearly distinct purpose: analyzing complexity, backtracking, architecture design, retrieving various templates (cell protocol, molecular, organ, prompt program, protocol shell, technique guide), symbolic abstraction, question decomposition, and logic verification. No overlapping functionality.
Most tools follow a verb_noun pattern (e.g., analyze_task_complexity, get_cell_protocol). However, 'backtracking' is a gerund and 'symbolic_abstract' combines an adjective with a verb, deviating slightly from the prevailing pattern.
With 12 tools, the server is well-scoped for its domain of context engineering. Each tool earns its place, covering analysis, design, template retrieval, and verification without being overwhelming or sparse.
The tool set covers core workflows: analyzing tasks, designing architecture, retrieving building blocks, and verifying logic. A minor gap is the lack of tools for executing or instantiating the designed blueprints, but the provided surface is largely complete for planning and template access.
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