Open-source developer tools for The Neural Ledger intelligence API.
TNL Intelligence gives AI agents and quantitative research systems structured, source-linked global event intelligence. It is an evidence and research layer, not a broker, an order router, or a source of trading-grade prices.
Package | Purpose |
| Typed TypeScript client for the TNL |
| Read-only MCP server over stdio or Streamable HTTP |
| Hosted OAuth, tenant policy, quota, and audit MCP gateway |
| Developer credentials, static sample API, and explorer |
| Webhook contracts, signed delivery, and verification helpers |
| Evidence-first skills, orchestration, MCP App, and web UI |
| Shared Cursor and OpenAI workflow contracts and rendering |
| Shared n8n, Pipedream, and Zapier action/trigger contracts |
|
|
| Python SDK and optional point-in-time quant research toolkit |
Related MCP server: alphai-news
Set a member API key in the environment:
export TNL_API_KEY="..."Run the MCP server over stdio:
npx -y @theneuralledger/mcpOr run its localhost Streamable HTTP endpoint:
docker compose up --build
curl http://127.0.0.1:7317/healthzUse the CLI:
npx -y @theneuralledger/cli latest
npx -y @theneuralledger/cli search "Federal Reserve"Use the TypeScript SDK:
import { TnlClient } from '@theneuralledger/sdk';
const client = new TnlClient({ apiKey: process.env.TNL_API_KEY! });
const page = await client.listNews({ sort: 'pipeline', pageSize: 20 });Use the Python SDK:
from tnl_intelligence import TnlClient
with TnlClient(api_key="...") as client:
page = client.list_news(sort="pipeline", page_size=20)API keys are never accepted as visible command-line arguments.
MCP tools are read-only and do not place trades.
Streamable HTTP binds to 127.0.0.1 by default.
Local daemon events never contain the API key.
Hosted deployments use the separate gateway, TLS ingress, OAuth validation, tenant policy, short-lived upstream capabilities, and distributed quotas.
See SECURITY.md and the build plan. Research deployment boundaries and staged rollout are documented in Research operations.
npm install
npm run openapi:sync
npm run validatePython development uses the project under python/tnl_intelligence.
MIT
Return event intelligence linked to an asset ticker. This is not a live trading-price tool.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| ticker | Yes | ||
| publishedSince | No | ISO 8601 lower bound for publication time |
| Name | Required | Description |
|---|---|---|
| data | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare read-only, idempotent, and non-destructive traits. The description adds the clarification that it is not a live price tool, which is a useful behavioral note beyond the 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?
Two sentences, no fluff, front-loaded with the key action. Every word earns its place.
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?
With an output schema present, the description need not detail return format, but it could explain what 'event intelligence' means and how to use parameters. The current description is adequate but minimal.
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 description does not explain any parameters (ticker, limit, publishedSince). Only publishedSince has a description in the schema, and coverage is low at 33%. The description adds no parameter meaning beyond the schema.
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 'event intelligence linked to an asset ticker' and explicitly clarifies it is not a live trading-price tool, distinguishing it from price-related tools among siblings.
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: use for asset event intelligence, not for live prices. It implies usage but does not explicitly compare with siblings like tnl_latest_news or tnl_search_news.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Run a question through TNL Ledger AI research orchestration and return its answer with citations and context.
| Name | Required | Description | Default |
|---|---|---|---|
| question | Yes |
| Name | Required | Description |
|---|---|---|
| data | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false, which inform the AI that the tool is safe, idempotent, and non-destructive. The description adds only that it uses 'research orchestration' and returns citations/context, which is useful but does not significantly expand on behavioral traits like potential latency or error conditions.
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, efficiently stating the action and output. It is front-loaded with the verb 'Run'. However, it lacks structural elements like bullet points or separate sections that could improve scanability, though it remains concise and to the point.
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?
An output schema exists (not shown), but the description does not mention any additional context such as expected response time, cost implications, or whether the tool can handle follow-up questions. The phrase 'with citations and context' is somewhat vague. Given the tool's apparent complexity, the description could provide more complete guidance for the AI.
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% for the single parameter 'question'. The description merely states that a question is passed, offering no additional meaning, format, or examples to compensate for the lack of schema documentation. The AI receives no guidance on what constitutes a well-formed question.
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 verb 'Run a question through' and the resource 'TNL Ledger AI research orchestration', and specifies the output 'answer with citations and context'. This differentiates it from sibling tools like tnl_latest_news or tnl_asset_intelligence, which are narrower in scope.
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 explicit guidance on when to use this tool versus its siblings (e.g., tnl_latest_news, tnl_search_news). There is no mention of prerequisites, exclusions, or alternative tools for specific use cases, leaving the AI to infer usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Return recent stories connected to a named TNL entity or entity id.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| entity | Yes | ||
| publishedSince | No | ISO 8601 lower bound for publication time |
| Name | Required | Description |
|---|---|---|
| data | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, providing a solid baseline. The description adds that it returns 'recent stories' and clarifies the entity parameter accepts either a name or an ID, which is helpful 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?
A single, well-structured sentence that front-loads the key action and resource. Every word earns its place; there is no extraneous information.
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 the tool having three parameters and an output schema, the description is sufficient for a straightforward query tool. It identifies the purpose, resource, and parameter semantics for entity. With output schema existing, return values do not need elaboration. Minor gap: no mention of default ordering or recency window.
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 only 33% (only publishedSince has a description). The description clarifies that the entity parameter can be a 'named TNL entity or entity id', which adds value. However, it does not explain the limit or publishedSince parameters beyond what the schema provides.
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 verb 'Return' and the resource 'recent stories connected to a named TNL entity or entity id'. This distinguishes it from sibling tools like tnl_search_news (full-text search) and tnl_asset_intelligence (asset-level intelligence).
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 like tnl_search_news or tnl_latest_news. An agent must infer usage from the name and brief description alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Ask Ledger AI to explain a TNL event using its evidence, contradictions, affected assets, and likely impact paths.
| Name | Required | Description | Default |
|---|---|---|---|
| focus | No | ||
| story | Yes | TNL story id or slug |
| Name | Required | Description |
|---|---|---|
| data | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. Description adds valuable context about the explanation's content (evidence, contradictions, affected assets, impact paths), which goes 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?
Single, well-structured sentence that front-loads the action ('explain a TNL event') and lists key aspects. No wasted 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?
Adequate for a read-only explanation tool with output schema, but lacks detail on the 'focus' parameter and usage scenarios. Could include example or clarify optional parameter.
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 only 50% (one of two parameters has a description). The tool description adds no meaning to parameters beyond what the schema provides. The 'focus' parameter is unexplained.
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?
Clearly states verb 'explain' and resource 'TNL event', and specifies what the explanation includes (evidence, contradictions, affected assets, impact paths). Distinguishes from siblings like tnl_impact_path which focuses solely on impact paths.
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?
Implies usage ('Ask Ledger AI to explain...') but provides no explicit guidance on when to use this tool vs. alternatives like tnl_impact_path or tnl_entity_intelligence.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Return stories associated with a causal or market impact path.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| impactPath | Yes | ||
| publishedSince | No | ISO 8601 lower bound for publication time |
| Name | Required | Description |
|---|---|---|
| data | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false, covering safety and idempotency. The description adds that it returns stories for impact paths, but does not disclose any additional traits like pagination, behavior on invalid input, or data freshness. With annotations carrying most of the load, a score of 3 is appropriate.
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 extraneous words. It efficiently states the tool's core function, earning its place without wasted characters.
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 having an output schema and strong annotations, the description is too minimal for a tool with three parameters. It does not define 'impact path,' set expectations on result scope or limitations, or provide any usage hints. The agent would need to infer too much from the name and schema alone.
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 only 33%, so the description should compensate. However, it does not explain any of the three parameters (impactPath, limit, publishedSince) beyond the schema's minimal details. The term 'impact path' is hinted in the purpose but not explicitly linked to the parameter, leaving agents with little beyond the schema.
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 verb 'return' and the resource 'stories' with the qualifier 'associated with a causal or market impact path.' It distinguishes from sibling tools like tnl_search_news and tnl_latest_news by focusing on impact paths, but does not explicitly differentiate or name 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 provides no guidance on when to use this tool versus siblings or when it is appropriate to use. There are no prerequisites, exclusions, or context cues to help an agent decide.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Return recent evidence-backed news intelligence, optionally filtered by category or country.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| country | No | ||
| category | No | ||
| publishedSince | No | ISO 8601 lower bound for publication time |
| Name | Required | Description |
|---|---|---|
| data | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint, idempotentHint, etc. Description adds that news is 'evidence-backed,' which supplements safety and idempotency context without contradicting 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?
Single sentence, front-loaded with the core action and optional filters. No extraneous information.
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?
Output schema exists but description omits details on limit and publishedSince parameters. Adequate for basic understanding but incomplete for a tool with 4 parameters.
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?
Description mentions optional category/country filtering but does not explain limit, publishedSince, or their formats. With only 25% schema description coverage, the description adds minimal parameter guidance.
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?
Clearly states the tool returns recent evidence-backed news intelligence with optional filtering by category or country. Differentiates from sibling tnl_search_news by focusing on 'latest' rather than search.
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?
Implies usage for obtaining recent news, but lacks explicit guidance on when to use this vs. siblings like tnl_search_news. No 'when not to use' or alternative recommendations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search TNL stories and return structured evidence, impacted assets, entities, and impact paths.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| query | Yes | ||
| country | No | ||
| category | No | ||
| publishedSince | No | ISO 8601 lower bound for publication time |
| Name | Required | Description |
|---|---|---|
| data | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, so the safety profile is clear. The description adds return types but does not disclose behavioral traits like pagination, result ordering, or handling of empty results. With annotations present, a 3 is appropriate.
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, which is concise but lacks structure. It front-loads the verb but does not organize information hierarchically. It could beneficially expand with minimal additional detail.
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 5 parameters and an output schema (present but not shown), the description is too sparse. It omits usage context, parameter explanations, and differentiation from siblings. The presence of an output schema does not excuse the lack of guidance for a search 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 description coverage is only 20% (only publishedSince has a description). The description does not explain the purpose of 'query', 'limit', 'country', or 'category'. An agent must infer their meanings from parameter names, which is insufficient for precise invocation.
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 verb 'Search' and resource 'TNL stories', and lists return types (structured evidence, impacted assets, entities, impact paths). However, it does not explicitly distinguish this tool from siblings like tnl_latest_news or tnl_deep_research, which could be ambiguous. The purpose is clear but lacks differentiation.
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 usage guidelines are provided. The description does not specify when to use this tool versus alternatives such as tnl_latest_news for recent stories or tnl_asset_intelligence for asset-specific search. An agent has no explicit guidance on selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Check API access, plan usage, market-context freshness, and current rate-limit headers.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
| data | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare read-only, idempotent, non-destructive, open-world. The description adds specific behavioral context (what exactly is checked: API access, plan usage, freshness, rate limits), which is valuable beyond the 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?
Single sentence, front-loaded with the action verb 'Check', no wasted words. All content is relevant and efficient.
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 zero-parameter health-check tool with an output schema, the description fully covers the scope of what the tool returns (four specific aspects). No gaps given the tool's simplicity.
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 exist, so schema coverage is 100%. Per guidelines, baseline for 0 params is 4; description adds no parameter info because none needed.
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 uses specific verb 'Check' and enumerates the exact aspects of service status (API access, plan usage, market-context freshness, rate-limit headers), clearly distinguishing it from sibling tools that focus on news or asset data.
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 explicit guidance on when to use this tool versus alternatives. The description implies it is for monitoring or pre-call checks, but lacks explicit 'when to use' or 'when not to use' statements.
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. Dates show when Glama detected each change.
v0.1.0tnl_asset_intelligencetnl_deep_researchtnl_entity_intelligencetnl_explain_eventtnl_impact_pathtnl_latest_newstnl_search_newstnl_service_statusEach tool has a clearly distinct purpose: news retrieval, search, asset/entity intelligence, impact paths, explanation, deep research, and status. There is no overlap or ambiguity.
All tools follow a consistent tnl_verb_noun or tnl_noun_phrase pattern in snake_case, making the naming predictable and easy to navigate.
With 8 tools, the server covers a broad range of intelligence functions without being bloated or too sparse. Each tool earns its place.
The tool set covers core workflows: retrieving news, searching, querying asset/entity intelligence, impact paths, explanations, deep research, and API status. No obvious gaps for the stated domain.
AI-enriched financial news for AI agents & trading bots: search, trending, insider, scored 1-10.
Real-time financial news for AI agents: search by ticker and source, with sentiment and entities.
Cross-source news, finance, AI and tech search across 29 sources for agents (BBC, NYT, CNBC, HF).
Real-time curated crypto news for AI agents with sentiment, recaps, and search.
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