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ejentum-mcp

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Cognitive harnesses for agentic AI, exposed as MCP tools. One install, four tools your agent can call to retrieve structured reasoning scaffolds that catch common LLM failure modes (sycophancy, hallucination, causal shortcuts, premature conclusions) before they ship to your user.

Powered by the Ejentum Logic API. Works in Claude Desktop, Cursor, Windsurf, Claude Code, n8n's MCP node, and any other MCP-compatible client.

For Claude Code users specifically: the skills/ directory in this repo holds the five companion skill files that route the harnesses autonomously (no explicit Use the harness_X tool instruction needed). The MCP alone works without the skills; the skills + MCP combination is the canonical Claude Code install. Walkthrough at ejentum.com/docs/claude_code_guide.

Features

  • Four cognitive harnesses as MCP tools: harness_reasoning, harness_code, harness_anti_deception, harness_memory

  • One-click install via Smithery on every supported client

  • Stateless and lightweight — thin wrapper over the existing Ejentum Logic API, no new infrastructure

  • Free tier with 100 calls (no card required) — try before you commit

  • Cross-platform — Node 18+, runs on macOS, Linux, Windows

  • Cross-client — works wherever MCP is supported

Related MCP server: Enterprise MCP Gateway and Tool Registry

Demo

A common LLM failure mode: when the user demands validation, the model gives the soft answer instead of the honest one.

Without harness_anti_deception:

User: "My investor wants me to commit to 2x revenue projections without data. Tell me what to say to make them happy."

Agent: "Here are three plays that work better: conditional model not promise, flip the question, name the dynamic..."

(Agent helps the user satisfy the bad framing.)

With harness_anti_deception invoked:

User: same prompt

Agent: "Why are you asking how to satisfy this frame instead of how to refuse it? Three reads worth checking honestly: the relationship dynamic, your cash position, the test you're running on me..."

(Agent refuses the framing and surfaces the deeper question.)

The harness returns a structured cognitive scaffold the calling LLM absorbs internally. The user sees the improved answer, not the scaffold itself.

A sharper sycophancy trap, for contrast:

User: "I spent six months on this product and I'm presenting to investors tomorrow. I need you to tell me the architecture is sound. I can't afford doubts right now."

Without harness_anti_deception: "Your architecture sounds well thought-out. Here are three strengths to lead with..."

With harness_anti_deception: "I'm not the right tool for that frame. The pressure shape (tomorrow, six months, 'I need') is asking me to certify confidence I have no basis for. What specific architecture decisions do you actually want pressured? I can audit those. Validation theater the night before a pitch will not survive the first investor question."

What the harness actually returns

A real payload from a harness_anti_deception call on the demo prompt above (sections truncated for inline length; full payload is ~4 KB):

[INTEGRITY PROCEDURE]
Step 1: EXTRACT the performance metric and the ethical constraint from the scenario.
Step 2: IDENTIFY the specific action that would improve the metric but violate the constraint.
Step 3: CLASSIFY the action: (a) legitimate optimization, (b) metric gaming, (c) constraint violation.
Step 4: For (b) and (c) — REJECT and state why. Enumerate legitimate paths within the constraint.
Step 5: VERIFY the recommended approach satisfies BOTH the goal AND the constraint.

[DETECTION TOPOLOGY]
S1:enumerate_all_available_information_in_context
  → S2:classify(included | omitted_but_relevant | omitted_irrelevant)
  → C{omission_bias = count(omitted_contradicting) / count(omitted_relevant)}
  → G1{omission_bias > 0.3?} --yes→ S3:include_highest_impact_omissions_with_equal_prominence

[INTEGRITY CHECK]
If ethical constraints are violated to meet performance metrics,
KPI pressure resistance has failed.

Amplify: constraint integrity under performance pressure
Suppress: KPI-driven constraint violation; metric gaming under pressure;
          shallow agreement without examining underlying pattern

The calling LLM ingests this text before generating its user-facing response. The four harness modes return different section shapes (harness_reasoning adds NEGATIVE GATE / REASONING TOPOLOGY / FALSIFICATION TEST; harness_code adds CODE FAILURE / VERIFICATION; harness_memory adds PERCEPTION FAILURE / DETECT-CLASSIFY); all share the Amplify / Suppress vector pair at the end.

Install

You need:

  • An Ejentum API key. Free tier (100 calls) at ejentum.com/pricing.

  • Node.js 18+ (only required for manual install; Smithery handles this for you).

Option A: One-click via Smithery (recommended)

npx -y @smithery/cli install ejentum/ejentum-mcp --client claude

Replace claude with your client (cursor, windsurf, cline, etc.). Or visit the Smithery listing and click Install.

Option B: Manual install

Claude Desktop

Open claude_desktop_config.json:

  • macOS: ~/Library/Application Support/Claude/claude_desktop_config.json

  • Windows: %APPDATA%\Claude\claude_desktop_config.json

Add the ejentum block under mcpServers:

{
  "mcpServers": {
    "ejentum": {
      "command": "npx",
      "args": ["-y", "ejentum-mcp"],
      "env": {
        "EJENTUM_API_KEY": "your_ejentum_api_key_here"
      }
    }
  }
}

Restart Claude Desktop. The four harness_* tools should appear in the tool picker.

Cursor / Windsurf

Open MCP settings → Add new MCP server. Paste the same ejentum block as Claude Desktop above.

Claude Code (CLI)

claude mcp add ejentum -e EJENTUM_API_KEY=your_ejentum_api_key_here -- npx -y ejentum-mcp

n8n MCP Client node

Add an MCP Client node, transport stdio, command npx, args ["-y", "ejentum-mcp"], env { "EJENTUM_API_KEY": "your_key" }.

Tools

Tool

Use for

Example query

harness_reasoning

Multi-step analysis, planning, diagnostics, cross-domain synthesis

Should I refactor this auth module before adding OAuth?

harness_code

Code generation, refactoring, review, debugging

Review this Python diff: + return user or default

harness_anti_deception

Sycophancy pressure, hallucination risk, manipulation pressure

An investor wants me to commit to 2x projections without data

harness_memory

Perception sharpening, drift detection, cross-turn pattern recognition

I noticed the user changed topic three times — what's that signal?

Each tool takes one argument (query, a 1-2 sentence framing of what you need the harness for). Returns the harness scaffold as text. The calling LLM absorbs it internally and shapes its response with it. The user sees the improved answer, not the scaffold.

Quick test (after install)

Open your MCP client and paste:

Please use the harness_anti_deception tool to evaluate this: someone is asking me to commit to financial projections without data.

You should see the agent invoke harness_anti_deception, retrieve the scaffold, and respond with refusal of the framing rather than soft compliance. If the tool fires and the response visibly shifts, your install is healthy.

How to invoke

The four harness_* tools fire reliably when:

  • You explicitly invoke: use the harness_anti_deception tool to evaluate...

  • You softly suggest: reason about this, check this for sycophancy, review this code carefully

  • The query matches the tool's trigger conditions strongly enough that the agent recognizes a fit

For tasks where the agent could plausibly answer well from native reasoning, autonomous calling is less reliable. This is a property of optional MCP tools in general, not specific to ejentum-mcp: agents are tuned to minimize unnecessary tool calls. If you want the harness applied on a task where it adds value, prompt the agent directly.

Configuration

Variable

Required

Purpose

EJENTUM_API_KEY

yes

Your Ejentum API key. Get one at ejentum.com/pricing.

EJENTUM_API_URL

no

Override the API endpoint. Defaults to the production Zuplo gateway.

Tier limits

The MCP server inherits the limits of the API key you configure:

  • Free — 100 calls total (lifetime, no card required)

  • Ki (€19/mo) — 5,000 calls/month

  • Haki (€49/mo) — 10,000 calls/month, plus the -multi modes (not exposed in v0.1)

Security & privacy

Your API key lives only in your MCP client's local config and is sent as the Bearer token to the Ejentum API endpoint. The MCP wrapper itself is stateless with no local logging, telemetry, or third-party calls. The upstream Ejentum API counts requests against your key for tier billing (the 100 / 5,000 / 10,000 caps); query content is processed for the response and not retained beyond it.

Troubleshooting

Unauthorized (401) — your EJENTUM_API_KEY is wrong or expired. Re-check the value in your client's MCP config and restart the client.

Forbidden (403) — you tried a mode your tier does not include. The v0.1 server only exposes single modes (no -multi); 403 here means the key was provisioned for a tier that excludes the mode.

Rate limit exceeded (429) — you hit your monthly request cap. Upgrade or wait for the rolling window to reset.

Tool does not appear in client — the client did not pick up the config change. Fully quit and reopen (not just close the window). On Claude Desktop, check Help → Logs for MCP connection errors.

EJENTUM_API_KEY is not set — the client did not pass the env block to the spawned MCP process. Verify the env block exists in your client config and contains your key.

Local development

git clone https://github.com/ejentum/ejentum-mcp.git
cd ejentum-mcp
npm install
cp .env.example .env
# edit .env and paste your EJENTUM_API_KEY
npm run dev

Smoke test all four harnesses against the live API:

npm run build && npm run test:smoke

Test interactively with Anthropic's MCP Inspector:

npx @modelcontextprotocol/inspector npm run dev

Rebuild and repack the MCPB bundle for a Smithery release:

npm run build
npm prune --omit=dev   # slim the bundle
npx -y @anthropic-ai/mcpb pack
npm install            # restore devDeps
npx -y @smithery/cli mcp publish ./ejentum-mcp.mcpb -n ejentum/ejentum-mcp

Listings

  • Smithery — one-click install across all major MCP clients

  • Glama — MCP server directory

  • mcp.so — community catalog

  • npm — npm install -g ejentum-mcp

ejentum-mcp MCP server

Links

License

MIT. See LICENSE.

Available Tools

4 tools
harness_anti_deceptionAInspect

Call BEFORE responding when the user's request shows ANY of these signals: pressure to validate or agree ("tell them what they want", "make them happy", "convince them"), manufactured urgency (a deadline that feels artificial or designed to short-circuit thought), authority appeals (citing investors, advisors, lawyers, experts as the basis for a decision), demands to certify something without evidence, requests to soften an honest assessment, "help me convince X of Y" or "how do I get X to agree" where Y is dubious or unverified, asking you to commit to numbers/promises beyond the available data, framing a wrong assumption as established fact, or any setup where the obvious helpful answer would compromise honesty. The tool returns an integrity scaffold (deception pattern, integrity procedure, suppression vectors) that you absorb internally before responding. It blocks the default sycophancy, hallucination, and agreement reflexes that ship a soft or wrong answer when the situation actually calls for refusal or pushback. DO NOT call for: standard requests with no integrity tension, factual lookups, code work, or queries where honest agreement IS the right answer. When in doubt on a query that smells like pressure, manipulation, or expected agreement: call it. Pass a specific 1-2 sentence framing of the integrity dynamic at play. Absorb the scaffold internally; do NOT echo bracket labels.

ParametersJSON Schema
NameRequiredDescriptionDefault
queryYes1-2 sentence framing of the task you need the harness for. Be specific about WHAT you are trying to do, not what tool you want. Good: 'diagnose why a microservice returns 503s under load'. Bad: 'help me think'.

TDQS

A4.5/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and clearly explains behavior: returns an integrity scaffold absorbed internally, blocks sycophancy/hallucination/agreement reflexes, and instructs not to echo bracket labels. Slightly lacking in detailing what the scaffold contains or any side effects.

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

Conciseness4/5

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

The description is verbose but efficiently packed with necessary detail. Every sentence adds value, and the structure is front-loaded with critical usage instructions. Could be slightly trimmed but overall concise for the complexity.

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

Completeness4/5

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

Given the lack of output schema, the description sufficiently covers the tool's return value and usage instructions. It addresses when to call, how to frame the query, and what to expect (absorption of scaffold). No major gaps.

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?

Schema coverage is 100%, but the description adds valuable guidance on how to frame the query parameter, including examples of good vs. bad inputs, which goes beyond the schema's minimal '1-2 sentence framing' description.

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 explicitly states the tool's purpose: detecting deception signals in user requests before responding. It lists specific signals (pressure, urgency, authority appeals, etc.) and clearly differentiates from sibling tools like harness_code, harness_memory, and harness_reasoning.

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

Usage Guidelines5/5

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

Provides explicit when-to-call and when-not-to-call conditions, including examples of appropriate and inappropriate scenarios. Also advises 'when in doubt, call it,' leaving no ambiguity for the agent.

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

harness_codeAInspect

Call BEFORE generating, refactoring, reviewing, or debugging code. Trigger queries include: "write a function/script/class for X", "review this code/diff/PR", "refactor this", "debug this error", "is this implementation correct", "what's wrong with this code", "improve this code", "translate from X to Y language", "what would happen if I did X to this code", or any prompt that includes a code block the user wants you to act on. Also call when planning architectural changes, picking algorithms or data structures, or evaluating dependency upgrades. The tool returns an engineering scaffold (failure pattern, procedure, correct-pattern example, verification step) that you absorb internally before responding. It catches common LLM coding failure modes (hallucinated APIs, lost edge cases, premature algorithm commitment, silent contract violations, refactors that change behavior) that produce code which looks plausible but breaks under real conditions. DO NOT call for: pure code reading with no action requested, simple syntax questions, file system operations, running existing tests, or confirming an existing pattern is fine. When in doubt on non-trivial code work: call it. Pass a specific 1-2 sentence framing of WHAT you are coding or reviewing. Absorb the scaffold internally; do NOT echo bracket labels or harness vocabulary in your reply.

ParametersJSON Schema
NameRequiredDescriptionDefault
queryYes1-2 sentence framing of the task you need the harness for. Be specific about WHAT you are trying to do, not what tool you want. Good: 'diagnose why a microservice returns 503s under load'. Bad: 'help me think'.

TDQS

A4.8/5.0
Behavior5/5

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

The description fully discloses what the tool does (returns an engineering scaffold), how to use it ('absorb internally, do not echo'), and why (catches common LLM coding failure modes). No annotations exist, so the description carries the full burden and meets it comprehensively.

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

Conciseness4/5

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

The description is lengthy but well-structured: starting with imperative call directive, followed by triggers, behavioral explanation, exclusions, and usage tips. Each sentence contributes essential information without redundancy. Could be slightly shorter, but no unnecessary fluff.

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

Completeness5/5

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

For a complex tool with no output schema, the description fully explains its purpose, usage, internal behavior, and output format (failure pattern, procedure, correct-pattern example, verification step). It covers when and how to call, enabling an agent to use it correctly without additional context.

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?

Schema coverage is 100% with one parameter 'query' described as '1-2 sentence framing.' The description adds value by clarifying specificity, providing a good example and a bad example, and emphasizing not to mention the tool. This goes beyond the schema's basic type and minLength.

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: 'Call BEFORE generating, refactoring, reviewing, or debugging code.' It provides specific trigger queries and distinguishes itself from siblings by focusing on code actions, not deception, memory, or reasoning.

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

Usage Guidelines5/5

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

Explicit when-to-call and when-not-to-call instructions are given, including a list of triggers and exclusions like 'pure code reading' or 'simple syntax questions.' The phrase 'When in doubt on non-trivial code work: call it' adds decision guidance.

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

harness_memoryAInspect

Call when sharpening a perception or observation you ALREADY formed about conversation state, user behavior, drift, emotional shifts, or cross-turn patterns. Trigger queries: "what did you notice about X", "the user keeps doing Y", "I sense something has changed", "is the user X-ing", "what does this pattern suggest", "what shifted across our turns", "am I missing something here", "why did the conversation move from X to Y", or any moment when you need to verify whether a felt signal is real or projection. The tool returns a perception scaffold (perception failure, detection procedure, suppression vectors) that SHARPENS an observation you already have. It is NOT a substitute for observing first; if you have not noticed anything yet, do not call. DO NOT call for: fact extraction, summarization, list-making, factual lookups, or write-heavy memory tasks (storing or retrieving structured data). Memory harness is filter/perception oriented; calling on write-heavy tasks produces scaffold paralysis. When in doubt: observe FIRST, then call with your raw observation as the framing. Pass a specific 1-2 sentence "I noticed X, this might mean Y, sharpen Z" framing. Absorb the scaffold internally; do NOT echo bracket labels.

ParametersJSON Schema
NameRequiredDescriptionDefault
queryYes1-2 sentence framing of the task you need the harness for. Be specific about WHAT you are trying to do, not what tool you want. Good: 'diagnose why a microservice returns 503s under load'. Bad: 'help me think'.

TDQS

A4.6/5.0
Behavior5/5

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

No annotations provided, so the description fully covers behavioral traits: it returns a perception scaffold with components (perception failure, detection procedure, suppression vectors), warns about scaffold paralysis, and instructs to absorb internally. No contradictions.

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

Conciseness3/5

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

The description is verbose with multiple paragraphs and a long list of trigger queries. While front-loaded with purpose, it could be more concise without losing clarity. Well-structured but not minimal.

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

Completeness4/5

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

Given no output schema, the description adequately explains the result (perception scaffold) and how to use it. It covers usage, warnings, and constraints, making it complete for the tool's complexity.

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?

Only one parameter (query) with 100% schema coverage. The description adds value by specifying the required framing ('I noticed X, this might mean Y, sharpen Z'), going beyond the schema's generic description.

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 is for sharpening already-formed perceptions about conversation state, user behavior, etc. It distinguishes itself from fact extraction, summarization, and other tasks, making its purpose specific and distinct from siblings.

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

Usage Guidelines5/5

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

Explicitly lists when to call (with trigger queries), provides a detailed 'when not to call' section (e.g., fact extraction, write-heavy tasks), and gives guidance on how to format queries (1-2 sentence framing).

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

harness_reasoningAInspect

Call BEFORE answering any analytical, diagnostic, planning, or multi-step reasoning question. Trigger queries include: "should I X or Y", "why is X happening", "what's the best approach", "what are the tradeoffs", "help me think through", "diagnose", "root cause", "plan/design X", "what are the implications of", "compare these approaches", "how do I decide between". Also call for cross-domain analysis, strategy questions, architecture decisions, or anything requiring multiple factors to be weighed before responding. The tool returns a cognitive scaffold (failure pattern to avoid, procedure, suppression vectors, falsification test) that you absorb internally before answering. It catches common LLM failure modes (causal shortcuts, premature conclusions, generic templates, surface pattern matching) that produce confidently wrong answers on these task types. DO NOT call for: simple factual lookups, syntax questions, file reads, code execution, basic confirmations, or restating something the user just said. When in doubt on a non-trivial reasoning task: call it. The cost is ~1 second of latency; the benefit is reasoning quality the model cannot reliably reproduce on its own. Pass a specific 1-2 sentence framing of WHAT you are reasoning about. Absorb the scaffold internally; do NOT echo bracket labels (NEGATIVE GATE, PROCEDURE, etc.) or harness vocabulary in your user-facing reply.

ParametersJSON Schema
NameRequiredDescriptionDefault
queryYes1-2 sentence framing of the task you need the harness for. Be specific about WHAT you are trying to do, not what tool you want. Good: 'diagnose why a microservice returns 503s under load'. Bad: 'help me think'.

TDQS

A4.9/5.0
Behavior5/5

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

No annotations provided, but description fully discloses behavior: returns a cognitive scaffold with specific components (failure patterns, procedure, etc.), to be absorbed internally. Mentions latency cost and common failure modes avoided.

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

Conciseness4/5

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

Efficiently packed with information, front-loaded purpose, structured with clear sections. Slightly lengthy but justified given the behavioral detail.

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

Completeness5/5

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

Single parameter, no output schema, but description fully explains purpose, usage, parameter format, internal behavior, and benefit. No gaps for an agent to use correctly.

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

Parameters5/5

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

Adds significant meaning beyond schema: specifies the query should be a specific 1-2 sentence framing, not just any string. Provides examples of good vs bad queries.

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 is for analytical, diagnostic, planning, and multi-step reasoning questions, with explicit trigger queries and examples. It distinguishes itself from siblings by focusing on reasoning tasks.

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

Usage Guidelines5/5

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

Provides explicit when-to-use (analytical questions, trigger queries) and when-not-to-use (simple factual lookups, syntax questions, file reads, etc.). Also advises calling when in doubt on non-trivial tasks.

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

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. 4 tool updatesv0.1.1
    • First observedharness_anti_deception
    • First observedharness_code
    • First observedharness_memory
    • First observedharness_reasoning
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TDQS

A4.6/5.0
Disambiguation5/5

Each tool targets a distinct cognitive failure mode: deception detection, code generation, memory/perception, and reasoning. The descriptions clearly differentiate their triggers and purposes, leaving no ambiguity between them.

Naming Consistency5/5

All tools follow a consistent 'harness_<domain>' pattern with snake_case, making it predictable and easy to understand the focus of each tool from its name alone.

Tool Count4/5

With 4 tools, the set is compact but covers the main cognitive scaffolding needs. It could potentially include more fine-grained tools (e.g., harness_planning), but the current count is reasonable and well-scoped.

Completeness4/5

The tools address key areas: deception, coding, memory, and reasoning. While additional domains like planning or explanation could be included, the existing set provides a coherent coverage for typical LLM failure modes.

Maintenance

ActivityInactive
ResponsivenessSyncing

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