Hourly crypto news sentiment and typed market events, with sources on every signal.
Scored across 4 tools
Each tool targets a distinct resource: discrete market events, current sentiment signals, historical sentiment series, and symbol universe metadata. There is no overlap that would cause an agent to pick the wrong tool for a given intent.
All tools follow a consistent get_<resource> snake_case pattern, and get_sentiment vs get_sentiment_history clearly distinguishes current vs historical data. The naming is predictable and discoverable.
Four tools is well-scoped for a focused market intelligence API: events, current sentiment, historical sentiment, and supported symbols. Each tool covers a necessary capability without redundancy.
The core current/historical sentiment workflow is complete, with symbol metadata and event alerts to support usage. The only notable gap is that event history is not exposed, so backtesting event-based signals would need to work around the 'recent' limitation.
Recent classified market events for a symbol. Kill-switch trio: hack_exploit, regulation, delisting — the reference integrations stand aside for 24h after one. Report the event headline and source link, not just a risk flag.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max events (default 20) | |
| symbol | Yes | One Shingou symbol, e.g. BTC-USD |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond annotations confirming read-only behavior, the description reveals the 'kill-switch trio' behavior where reference integrations stand aside for 24 hours, adding important behavioral context.
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 very concise, using two sentences plus a directive to convey purpose, behavioral quirks, and output expectations without unnecessary 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?
Given rich annotations, full schema documentation, and the lack of output schema, the description adequately explains what the tool does, how to use it, and what to report from the response.
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 100%, but the description adds value by noting the default limit (20) and providing an example symbol format, which are not fully captured in schema descriptions.
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 retrieves 'Recent classified market events for a symbol' and distinguishes itself from siblings like sentiment tools by specifying event types and reporting expectations.
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 usage guidance by instructing to report the event headline and source link, but it does not explicitly state when not to use this tool or suggest alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Latest news-sentiment signal per symbol (batch up to 50 in one call — one signal per asset per hour, so never poll faster than hourly). On the free plan, symbols outside BTC-USD/ETH-USD/SOL-USD are 24h delayed: check timestamp and disclose staleness.
| Name | Required | Description | Default |
|---|---|---|---|
| symbols | Yes | Shingou symbols, e.g. ["BTC-USD","ETH-USD"] (see get_symbols) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Adds significant behavioral info beyond annotations: batching limit, rate limit (hourly), free plan delay, timestamp disclosure requirement. No contradictions with readOnlyHint/idempotentHint.
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 concise sentences, each dense with actionable information. Front-loaded with main purpose and batching. 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?
Covers key aspects: batching, rate limits, plan differences, timestamp. Lacks explanation of return value structure, but acceptable for simple signal tool without output schema.
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 covers 100% of parameters. Description adds symbol format ('Shingou symbols'), example, and reference to get_symbols. Baseline 3 plus extra value.
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 it returns 'Latest news-sentiment signal per symbol' with batching up to 50. Distinguishes from sibling 'get_sentiment_history' via 'latest' and from 'get_symbols' by purpose.
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: batching allowed, polling frequency (hourly). Notes free plan delays and need to check timestamp. Does not explicitly contrast with get_events, but provides clear context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Point-in-time sentiment series for backtesting: bucket is the as-of time, no lookahead. from/to are clamped to your plan's history window and to its per-request span, then echoed back — check them rather than assuming. reconstructed: true buckets were rebuilt from archival news, not live-collected; disclose the mix in any backtest.
| Name | Required | Description | Default |
|---|---|---|---|
| to | Yes | Range end, ISO 8601 | |
| from | Yes | Range start, ISO 8601, e.g. 2026-06-27T00:00:00Z | |
| symbol | Yes | One Shingou symbol, e.g. BTC-USD | |
| interval | No | Bucket size (default 1h) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description goes well beyond the annotations (readOnly, openWorld, idempotent). It discloses three behavioral traits: no lookahead, clamping of from/to to plan limits, and the reconstructed flag meaning archival vs live-collected data. This adds critical context for backtesting integrity that annotations alone do not 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?
Three dense sentences, each earning its place: purpose + no-lookahead, clamping behavior, and reconstructed flag. Front-loaded and focused. No filler words or repetition of schema details.
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?
Despite no output schema, the description tells the user the key return semantics (bucket as-of time, reconstructed flag) and range clamping. It addresses the main risks (lookahead bias, range truncation, data provenance) for a backtesting tool. Sibling context and annotations complement it, making the description 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?
Schema already covers all 4 parameters with descriptions (100% coverage), so baseline is 3. The description adds meaningful context for 'from'/'to' by explaining they are clamped and echoed back, and clarifies the output field 'bucket' as the as-of time. This extra value justifies a 4.
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 provides a 'point-in-time sentiment series for backtesting', specifying both the resource (sentiment history) and its distinct purpose (backtesting) with the critical no-lookahead guarantee. This distinguishes it from sibling tools like get_sentiment (likely current sentiment) and get_events.
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 frames this tool for backtesting, giving a clear context for when to use it. It does not explicitly name alternatives or state when not to use it, but the backtesting context implies this is the historical counterpart to real-time sentiment tools. Minor deduction for lack of explicit exclusionary guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
The supported symbol universe with each symbol's free-plan freshness. Static — costs no quota and needs no API key.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only and idempotent. Description adds value by stating no quota cost and no API key requirement, and the static 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?
Two sentences, no fluff. Front-loaded with purpose. Every word serves a 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 zero parameters, rich annotations, and simple purpose, the description fully covers the tool's behavior and context.
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?
No parameters to describe. Description appropriately does not add parameter info since none exist. Baseline of 4 applies.
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 the supported symbol universe with free-plan freshness. It differentiates itself from sibling tools (get_sentiment, etc.) by focusing on symbols.
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?
Description mentions it is static, costs no quota, and needs no API key, implying low-risk usage. Lacks explicit exclusions or alternatives but context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
get_sentiment1 field changedInput schema / properties / symbols / descriptionPrevious value: -"Shingou symbols, e.g. [\"BTC-USD\",\"ETH-USD\"] (see list_symbols)"New value: +"Shingou symbols, e.g. [\"BTC-USD\",\"ETH-USD\"] (see get_symbols)"get_symbolslist_symbolsget_eventsget_sentimentget_sentiment_historylist_symbolsCrypto market signals, technical indicators, and sentiment analysis for AI agents.
Real-time curated crypto news for AI agents with sentiment, recaps, and search.
Real-time digital asset narrative intelligence from 1,000+ curated media sources.
Crypto market intelligence: regime detection, funding rates, liquidations, prices, signals.
