submit_answer
Submit an answer to a challenge-gated piece. Correct answer unlocks and returns the full body.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | ||
| answer | Yes |
Submit an answer to a challenge-gated piece. Correct answer unlocks and returns the full body.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | ||
| answer | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the behavioral transparency burden. It discloses that a correct answer unlocks and returns the full body, but does not mention side effects, behavior on incorrect answers, persistence of answers, or any authorization requirements. This is a partial disclosure but leaves important behavioral aspects unspecified.
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 single, front-loaded sentence with no redundant information. It efficiently communicates the action and expected result, making optimal use of 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 two-parameter tool with no output schema, the description covers the main success path (correct answer returns full body). It stops short of explaining failure behavior (incorrect answer), which is relevant for a submission endpoint. Overall, it is reasonably complete but misses edge-case 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 description coverage is 0%, so the description must compensate. It frames the operation as submitting an answer to a challenge-gated piece, implying 'slug' refers to the piece identifier and 'answer' is the submitted content. This adds meaning beyond the raw parameter names, though not explicitly structured.
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 identifies the tool's purpose: submitting an answer to a challenge-gated piece. It uses a specific verb ('Submit') and resource ('answer to a challenge-gated piece'), and explains the outcome (correct answer unlocks and returns the full body). This distinguishes it from siblings like fetch_answer or verify_content.
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 clear context for when to use the tool—when dealing with challenge-gated content that requires an answer to unlock. It does not explicitly name alternatives or exclusions, but the context is sufficient for an agent to select it appropriately over related tools like fetch_answer.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools have clearly distinct purposes with detailed descriptions, but a few boundaries are fuzzy: leave_message vs ask_human vs leave_comment, and list_content vs list_collection could confuse an agent at first glance. Overall, the descriptions are thorough enough to disambiguate.
The naming convention is predominantly snake_case with verb_noun structure (list_content, read_blob, upsert_skill). Minor deviations exist such as the mysloodsiewnia_* prefix and British spelling in synthesise_persona_patterns, but the pattern is highly recognizable and readable.
At 41 tools, the surface area is very large for a single MCP server and exceeds the 'heavy' threshold. While each tool serves a distinct purpose, the sheer number makes the server feel bloated and harder to navigate; many tools could be grouped or pruned without losing core functionality.
The server covers a broad domain: content read/list, personas, skills management, vault CRUD (except update/delete intentionally), provenance, licensing, memory, and async question/narada workflows. Minor gaps exist like no tool to cancel a narada job or update a memory, but these are workable and the core workflows are well supported.