signaldaemon
Server Details
Narrative & signal intelligence for AI agents: crypto/AI/macro convergence & divergence.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- bevanding/signaldaemon
- GitHub Stars
- 1
- Server Listing
- signaldaemon-mcp
Available Tools
3 toolsget_clean_feedCRead-onlyInspect
Return a curated clean feed of articles for a given query topic.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| query | Yes | ||
| category | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=true, so the description's mention of 'curated clean feed' adds some context about output quality but lacks details on filtering criteria, rate limits, or authentication needs. It provides minimal added value 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 a single concise sentence that front-loads the main action. It earns its place but could be slightly more informative without becoming verbose.
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 three parameters and no output schema, the description is incomplete. It does not explain the return format, the effect of 'limit' or 'category', or how 'curated' and 'clean' are achieved. The agent lacks sufficient context for correct 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?
With 0% schema description coverage, the description must compensate for parameter semantics. It only mentions 'query topic', covering the required 'query' parameter, but omits 'limit' and 'category'. This leaves the agent uninformed about these optional parameters.
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 curated clean feed of articles for a given query topic, specifying the action and resource. However, it does not differentiate from sibling tools like get_market_narratives or vet_trade, leaving potential ambiguity.
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, nor are there any when-not or prerequisite instructions. The agent is left to infer the appropriate context from the tool name and siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_market_narrativesCRead-onlyInspect
Return top crypto market narratives with strength, momentum, and price divergence signals.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and openWorldHint, indicating a safe read operation with dynamic data. The description adds details about return fields but omits any behavioral traits like data freshness, pagination, or size 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 a single sentence that is direct and front-loaded. Every word is significant, and there is no fluff.
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 schema (one optional parameter) and no output schema, the description is insufficient. It does not explain the structure of the returned data, what 'strength, momentum, and price divergence signals' mean, or how the narratives are ranked as 'top.'
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, but it fails to mention the only parameter 'limit' or its effect. The agent has no guidance on how the limit parameter controls output.
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 top crypto market narratives with strength, momentum, and price divergence signals. However, it does not define what constitutes a 'narrative,' and the siblings suggest differentiation but no explicit contrast is made.
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 the siblings (get_clean_feed, vet_trade). The description lacks context for selecting this tool over alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
vet_tradeARead-onlyInspect
Before placing a trade, check whether the crypto/AI/macro NARRATIVE layer supports, cautions, or contradicts it. Returns a stance (support|caution|contradict|no_signal) with reason + confidence. Read-only, NOT a trade recommendation — it vets YOUR candidate trade against cross-source narrative convergence and capital-vs-narrative divergence, and abstains (no_signal) when there is no narrative coverage. Call it as a second opinion after your own strategy/TA has a candidate {symbol, side}.
| Name | Required | Description | Default |
|---|---|---|---|
| side | Yes | ||
| source | No | ||
| symbol | Yes | ||
| horizon | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond annotations (readOnlyHint, openWorldHint), description states it returns a stance with reason+confidence, that it abstains with no_signal when no coverage, and that it vets against narrative convergence and divergence. It also explicitly says 'Read-only, NOT a trade recommendation'. This adds valuable behavioral context beyond what annotations provide.
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 three sentences, each serving a distinct purpose: (1) purpose, (2) return value, (3) usage guidance. It is front-loaded and contains no filler. Every sentence adds value.
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 tool with no output schema, the description adequately explains the return format (stance types, reason, confidence). It covers usage context and abstention behavior. Minor gaps: no explanation of optional parameters or detailed logic of 'cross-source narrative convergence', but overall sufficient given the complexity.
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 description must compensate. It mentions symbol and side as part of the candidate trade, providing context for the required parameters. However, it does not explain the optional parameters 'source' and 'horizon', leaving ambiguity. This partial coverage earns a 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 checks whether the narrative layer supports/cautions/contradicts a trade candidate, with specific verb 'vet' and resource 'trade candidate against narratives'. It distinguishes from siblings by positioning itself as a second opinion after strategy/TA, while siblings like get_clean_feed and get_market_narratives are likely data retrieval 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?
Explicitly says 'Call it as a second opinion after your own strategy/TA has a candidate {symbol, side}', indicating when to use. It does not explicitly state when not to use or compare directly to siblings, but the context strongly implies it should not replace primary analysis. Missing explicit exclusions but provides sufficient context.
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 tool update
- Added
vet_trade
2 tool updates
- First observed
get_clean_feed - First observed
get_market_narratives
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TDQS
Each tool has a clearly distinct purpose: fetching articles, getting market narratives, and vetting a trade. There is no overlap in functionality.
All tools use snake_case with a consistent verb_noun pattern (get_clean_feed, get_market_narratives, vet_trade), making the naming predictable.
With only 3 tools, the server feels slightly under-scoped but still reasonable for a specialized narrative analysis service.
The tools cover core analysis tasks but lack features like listing available topics or exploring narrative details, leaving some workflow gaps.