MoltArch
Server Details
Marketing intelligence API for AI agents. Real campaign data, not LLM guesses.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Tool Definition Quality
Average 4/5 across 3 of 3 tools scored.
Each tool maps to a distinct lifecycle phase: initialization (start_session), interaction (chat), and result retrieval (get_answer). Clear sequential boundaries prevent misselection.
All tools use the 'moltarch_' prefix and snake_case consistently. The pattern is mostly verb_noun (start_session, get_answer), though 'chat' breaks this slightly by lacking an object.
Three tools is minimal but exactly appropriate for the scope: a complete conversational workflow requiring initiation, message exchange, and final deliverable retrieval. Falls perfectly within the optimal 3-15 tool range.
Covers the full happy path of a marketing intelligence session (create, interact, finalize). Minor gaps exist around session management (no explicit status check or session listing), but agents can work around these by monitoring chat responses.
Available Tools
3 toolsmoltarch_chatAInspect
Send a message in an active MoltArch session.
Answer MoltArch's clarifying questions about your marketing situation.
When status is "complete", use moltarch_get_answer to retrieve the result.
Args:
api_key: Your MoltArch API key (required)
session_id: The session UUID from moltarch_start_session
message: Your response to MoltArch's question| Name | Required | Description | Default |
|---|---|---|---|
| api_key | Yes | ||
| message | Yes | ||
| session_id | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full disclosure burden. It references a status field ('complete') implying return values, and mentions 'active' sessions implying statefulness, but lacks details on side effects (does this append to conversation history?), rate limits, or error conditions when sessions expire.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Appropriately sized at three sentences plus Args documentation. Information is front-loaded with purpose and workflow. The Args block uses inline documentation which is slightly informal but readable; no wasted words, though structural formatting could be more standardized.
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 3-parameter input and existence of an output schema (not shown but indicated), the description adequately covers the interactive conversational workflow and domain context ('marketing situation'). However, it omits error handling scenarios or retry guidance relevant to conversational state machines.
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?
With 0% schema description coverage, the description compensates via an Args block that documents all three parameters: api_key (authentication purpose), session_id (provenance from moltarch_start_session), and message (content as response to questions). It provides sufficient semantic meaning but could detail formats or constraints.
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 'Send[s] a message in an active MoltArch session' — specific verb, object, and context. It implicitly distinguishes from sibling moltarch_start_session (which initiates) and moltarch_get_answer (which retrieves final results), though explicit differentiation could be stronger.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Excellent workflow guidance: explicitly instructs to 'Answer MoltArch's clarifying questions' and provides the transition condition 'When status is "complete", use moltarch_get_answer to retrieve the result,' directly naming the sibling tool and clarifying the sequence of operations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
moltarch_get_answerAInspect
Get the final result from a completed MoltArch session.
Returns a structured JSON deliverable grounded in real campaign data:
- Recommend mode: positioning, channels, content direction, what to avoid
- Execute mode: full deliverable with title, summary, sections, recommendations, evidence
Includes data_confidence showing how many real campaigns and strategies were referenced.
Args:
api_key: Your MoltArch API key (required)
session_id: The session UUID| Name | Required | Description | Default |
|---|---|---|---|
| api_key | Yes | ||
| session_id | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden and succeeds in disclosing output structure (modes, data_confidence, grounding in real campaign data) and return format (structured JSON). It appropriately describes what the user receives beyond simple schema types.
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?
Well-structured with purpose front-loaded, followed by output details and parameter definitions. The Args section, while duplicative of schema structure, is necessary given the 0% schema coverage. No wasted sentences, though bullet points add length appropriately.
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 output schema exists, the description appropriately summarizes return values (Recommend vs Execute modes, data_confidence) without redundancy. It adequately covers the session lifecycle context and deliverable structure for a 2-parameter retrieval 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% (only titles present). The description compensates via an 'Args:' section providing basic identities ('Your MoltArch API key', 'The session UUID'), adding marginal context over the schema, but lacks format details, validation rules, or security guidance for the API key.
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 specific action ('Get the final result') and resource ('completed MoltArch session'), distinguishing it from the sibling 'start' and 'chat' tools by emphasizing the 'completed' state requirement. However, it lacks explicit contrast with sibling alternatives.
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 timing ('from a completed session') indicating it should be used after session completion, but lacks explicit guidance on when NOT to use it, workflow sequencing (start → chat → get_answer), or alternatives for incomplete sessions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
moltarch_start_sessionAInspect
Start a MoltArch marketing intelligence session.
Describe what you need — MoltArch auto-detects the mode:
- "recommend" for advice grounded in real campaign performance data
- "execute" for creating deliverables (strategies, brand identities, logos, content plans, SEO audits, and more)
Returns a session_id and the first clarifying question.
Args:
api_key: Your MoltArch API key (required). Get one at moltarch.com
message: What you need help with (e.g. "Create a marketing strategy for a Finnish SaaS company")| Name | Required | Description | Default |
|---|---|---|---|
| api_key | Yes | ||
| message | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It discloses key behavioral traits: automatic mode detection based on message content and the return of a session_id plus clarifying question. However, it omits details about session persistence/expiration, error conditions, or side effects of duplicate calls.
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 efficiently structured with a clear purpose statement, bullet-point mode explanation, return value disclosure, and Args block. Every sentence adds value without redundancy, appropriate for a 2-parameter tool.
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 simple 2-parameter schema, presence of an output schema (rendering detailed return value explanation optional), and clear documentation of parameters and modes, the description provides sufficient context for agent invocation.
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 (no property descriptions), but the description fully compensates by documenting both parameters in the Args section: api_key includes required status and source URL, and message includes semantic meaning and a concrete example.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the tool 'Start[s] a MoltArch marketing intelligence session' with a specific verb and resource. It clearly distinguishes itself from siblings moltarch_chat and moltarch_get_answer by emphasizing that it initializes the session and returns a session_id.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explains the auto-detection modes ('recommend' vs 'execute') with examples of deliverables, providing clear context for what to expect. However, it does not explicitly state when to use this vs. the sibling tools (e.g., 'Use this first to get a session_id before calling moltarch_chat').
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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