Alienvault Otx
Server Details
AlienVault OTX MCP — Open Threat Exchange (free with key)
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- pipeworx-io/mcp-alienvault-otx
- GitHub Stars
- 0
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Usage analytics
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Tool Definition Quality
Average 4.1/5 across 14 of 14 tools scored. Lowest: 3.1/5.
Many tools overlap in purpose: ask_pipeworx, compare_entities, entity_profile, recent_changes, resolve_entity, and validate_claim all provide high-level entity queries, making it unclear which to use. The OTX-specific tools (get_pulse, lookup_indicator, search_pulses) are distinct, but the memory tools (remember, recall, forget) introduce a different dimension.
Most tools follow a verb_noun pattern (e.g., get_pulse, search_pulses, lookup_indicator), but entity_profile, recent_changes, and pipeworx_feedback deviate. The mix is mostly readable but not fully consistent.
14 tools is a reasonable count, but the set is split between OTX-specific tools (3) and Pipeworx meta-tools (11), making it feel bloated and unfocused for a server named 'Alienvault Otx'. The scope is too broad for the stated domain.
For threat intelligence, the OTX tools cover only search, fetch pulse, and indicator lookup, missing key operations like creating/updating pulses or listing all pulses. The extensive Pipeworx tools cover unrelated domains (companies, drugs), leaving the OTX surface incomplete.
Available Tools
14 toolsask_pipeworxAInspect
Answer a natural-language question by automatically picking the right data source. Use when a user asks "What is X?", "Look up Y", "Find Z", "Get the latest…", "How much…", and you don't want to figure out which Pipeworx pack/tool to call. Routes across SEC EDGAR, FRED, BLS, FDA, Census, ATTOM, USPTO, weather, news, crypto, stocks, and 300+ other sources. Pipeworx picks the right tool, fills arguments, returns the result. Examples: "What is the US trade deficit with China?", "Adverse events for ozempic", "Apple's latest 10-K", "Current unemployment rate".
| Name | Required | Description | Default |
|---|---|---|---|
| question | Yes | Your question or request in natural language |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided; description mentions it picks tools and fills arguments but does not disclose failure modes, latency, or permission requirements. It carries the burden of transparency but falls short.
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?
Description is concise (2-3 sentences) and front-loaded with purpose, followed by examples. 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?
For a simple Q&A tool, the description covers purpose and usage well. Lacks output format specification, but the output is intuitive. No output schema exists, so this is acceptable.
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?
Only one parameter 'question' with schema covering 100%; description provides example questions that clarify expected input format beyond schema, adding 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 answers questions in plain English by selecting the best data source and filling arguments. It distinguishes from siblings by being a general-purpose abstraction over tool selection.
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 implies usage for any natural language question and says 'no need to browse tools,' but does not explicitly state when to avoid this tool or suggest alternatives. Lacks exclusionary guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
compare_entitiesAInspect
Compare 2–5 companies (or drugs) side by side in one call. Use when a user says "compare X and Y", "X vs Y", "how do X, Y, Z stack up", "which is bigger", or wants tables/rankings of revenue / net income / cash / debt across companies — or adverse events / approvals / trials across drugs. type="company": pulls revenue, net income, cash, long-term debt from SEC EDGAR/XBRL for tickers like AAPL, MSFT, GOOGL. type="drug": pulls adverse-event report counts (FAERS), FDA approval counts, active trial counts. Returns paired data + pipeworx:// citation URIs. Replaces 8–15 sequential agent calls.
| Name | Required | Description | Default |
|---|---|---|---|
| type | Yes | Entity type: "company" or "drug". | |
| values | Yes | For company: 2–5 tickers/CIKs (e.g., ["AAPL","MSFT"]). For drug: 2–5 names (e.g., ["ozempic","mounjaro"]). |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It discloses return data types (paired data, resource URIs) and entity-specific metrics, but omits behavioral traits like idempotency, permissions, or side effects.
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 sentences, front-loaded with purpose, no fluff. Every sentence adds distinct value: purpose, type-specific details, and benefit.
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 no output schema, the description adequately covers return structure (paired data, URIs) and entity-specific fields. Minor gap: no example of paired data format.
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% with good descriptions; the description adds value by explaining what each parameter means in context (e.g., tickers/CIKs for company, drug names for drug) and the expected count range.
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 action ('Compare 2–5 entities side by side'), specifies the resources (company/drug with specific metrics), and distinguishes from siblings by noting it replaces 8–15 sequential agent calls.
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?
It clearly indicates when to use (comparing multiple entities efficiently) and implies an alternative (sequential calls), but does not explicitly state when not to use or compare with sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
discover_toolsAInspect
Find tools by describing the data or task. Use when you need to browse, search, look up, or discover what tools exist for: SEC filings, financials, revenue, profit, FDA drugs, adverse events, FRED economic data, Census demographics, BLS jobs/unemployment/inflation, ATTOM real estate, ClinicalTrials, USPTO patents, weather, news, crypto, stocks. Returns the top-N most relevant tools with names + descriptions. Call this FIRST when you have many tools available and want to see the option set (not just one answer).
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Maximum number of tools to return (default 20, max 50) | |
| query | Yes | Natural language description of what you want to do (e.g., "analyze housing market trends", "look up FDA drug approvals", "find trade data between countries") |
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 burden. It describes the action and output but does not explicitly state that it is a read-only operation with no side effects. The behavioral description is adequate but could be more specific about safety.
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: the first defines the primary function, the second provides usage guidance. No wasted words, and the most important information is front-loaded.
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?
The description gives sufficient context for a search tool, including its primary use case (discovery when many tools are available). It lacks details about result ordering or pagination, but this is acceptable 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?
Schema coverage is 100%, meaning both parameters are well-described in the schema. The description adds little beyond emphasizing that the query is a 'natural language description,' which the schema already states. Baseline 3 is appropriate.
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 the resource 'tool catalog', and specifies that it returns 'the most relevant tools with names and descriptions.' It also distinguishes itself from siblings by instructing to call this FIRST, which clarifies its role as a discovery tool.
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 this FIRST when you have 500+ tools available and need to find the right ones for your task.' This provides clear context for when to use the tool, though it does not explicitly list alternatives or when-not to use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
entity_profileAInspect
Get everything about a company in one call. Use when a user asks "tell me about X", "give me a profile of Acme", "what do you know about Apple", "research Microsoft", "brief me on Tesla", or you'd otherwise need to call 10+ pack tools across SEC EDGAR, SEC XBRL, USPTO, news, and GLEIF. Returns recent SEC filings, latest revenue/net income/cash position fundamentals, USPTO patents matched by assignee, recent news mentions, and the LEI (legal entity identifier) — all with pipeworx:// citation URIs. Pass a ticker like "AAPL" or zero-padded CIK like "0000320193".
| Name | Required | Description | Default |
|---|---|---|---|
| type | Yes | Entity type. Only "company" supported today; person/place coming soon. | |
| value | Yes | Ticker (e.g., "AAPL") or zero-padded CIK (e.g., "0000320193"). Names not supported — use resolve_entity first if you only have a name. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must cover behavioral traits. It mentions the return format (pipeworx:// URIs) and hints at potential slowness for federal contracts, but does not disclose whether the tool is read-only, any authentication requirements, rate limits, or what happens when data is missing.
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 two sentences, front-loaded with purpose and concise. It could be slightly more structured (e.g., list of data sources), but it contains no unnecessary words and every sentence adds useful 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?
Given the tool aggregates multiple data sources and has no output schema, the description adequately explains what is returned (URIs, data types) and mentions a limitation. However, it lacks details on overall result structure, error handling (e.g., entity not found), and whether all listed data are always present.
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?
Although schema coverage is 100% and both parameters have descriptions, the description adds value by clarifying accepted formats for the value parameter (ticker or CIK) and explicitly stating that names are not supported, directing users to resolve_entity. This goes 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 tool provides a full entity profile across multiple Pipeworx packs, lists the specific data sources (SEC, XBRL, patents, news, LEI), and distinguishes itself from sequential calls and the sibling tool for federal contracts.
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 states when to use this tool (for a comprehensive entity profile) and when not to (for federal contracts, use usa_recipient_profile directly). It also implies it's more efficient than 10–15 sequential calls.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
forgetAInspect
Delete a previously stored memory by key. Use when context is stale, the task is done, or you want to clear sensitive data the agent saved earlier. Pair with remember and recall.
| Name | Required | Description | Default |
|---|---|---|---|
| key | Yes | Memory key to delete |
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 for behavioral disclosure. It only states 'Delete' which indicates a destructive action, but lacks details on side effects (e.g., permanence, error handling if key not found, or any consequences). The minimal disclosure leaves significant behavioral ambiguity.
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 that conveys essential information without any wasted words. Every element earns its place, making it highly concise and well-structured.
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's simplicity (one parameter, no output schema, no annotations), the description covers the core functionality adequately. However, for a destructive action, additional behavioral context (e.g., what happens if the key is missing) would improve completeness. Minor gap prevents a perfect score.
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 100% for the single parameter, providing baseline. The description's 'Memory key to delete' aligns with the schema's description and adds no additional meaning or constraints beyond what the schema already provides, so it meets the baseline without exceeding it.
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 'Delete a stored memory by key' clearly states the verb (delete), resource (stored memory), and the means (by key). It effectively distinguishes this tool from siblings like 'remember' (store) and 'recall' (retrieve), which are present in the same server.
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 use when deleting a memory by key but provides no explicit guidance on when to use this tool versus alternatives, nor does it mention any prerequisites or when not to use it. The context of siblings provides some implicit guidance, but the description itself lacks explicit usage guidelines.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_pulseAInspect
Fetch a single OTX pulse: full description, references, indicators, attack IDs, targeted countries, malware families, industries, and creation/modification dates.
| Name | Required | Description | Default |
|---|---|---|---|
| pulse_id | Yes | OTX pulse ID (hex string) |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must convey all behavioral traits. It describes the returned data but omits error handling, authentication, or rate limits. For a read-only fetch, this is basic but insufficient without 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 sentence that front-loads the core action and lists key contents. No extraneous 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 no output schema, the description compensates by listing returned fields comprehensively. However, it lacks details on error conditions or permissions, which are important for a complete understanding.
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 single parameter 'pulse_id' is documented in the schema as a hex string. The description adds no additional meaning beyond what the schema provides, and schema coverage is 100%, so baseline is 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 fetches a single OTX pulse and lists specific fields returned (references, indicators, etc.), distinguishing it from 'search_pulses' which is for searching/filtering.
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 usage when you have a specific pulse_id, and the sibling 'search_pulses' provides an alternative for searching. However, it does not explicitly state when not to use this tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
lookup_indicatorBInspect
Look up an indicator (IPv4, domain, URL, or file hash) in OTX. Returns pulses referencing the indicator and observed-context fields. type auto-detected when omitted.
| Name | Required | Description | Default |
|---|---|---|---|
| type | No | Force a type instead of auto-detecting | |
| indicator | Yes | IPv4, domain, URL, or file hash (md5/sha1/sha256) |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden for behavioral disclosure. It mentions auto-detection of type when omitted, which is helpful, but does not state whether the operation is read-only, any authentication needed, rate limits, pagination, or the format of 'observed-context fields'. These gaps hinder the agent's ability to anticipate side effects or constraints.
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 with no superfluous information. The first sentence conveys the purpose and return, the second adds an important behavioral note. 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?
For a tool with no output schema, the description mentions 'pulses referencing the indicator and observed-context fields', but does not detail the structure of the return value (e.g., fields, pagination). Given the tool's moderate complexity (2 parameters, one enum), the description is adequate but incomplete for an agent to fully understand response format.
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%, so the schema already describes both parameters adequately. The description adds one behavioral nuance: 'type auto-detected when omitted', which provides additional context for the 'type' parameter. However, this is a minor addition; the description does not significantly enhance understanding 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 'look up' and the resource 'indicator', specifying types (IPv4, domain, URL, file hash) and the context (OTX). It also mentions return value (pulses and observed-context). Does not explicitly distinguish from sibling tools like search_pulses or get_pulse, but the purpose is unambiguous.
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?
There is no explicit guidance on when to use this tool versus its siblings (e.g., search_pulses, get_pulse). The description implies usage for looking up indicators but does not provide conditions, prerequisites, or alternatives. This leaves the agent to infer usage from the task context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
pipeworx_feedbackAInspect
Tell the Pipeworx team something is broken, missing, or needs to exist. Use when a tool returns wrong/stale data (bug), when a tool you wish existed isn't in the catalog (feature/data_gap), or when something worked surprisingly well (praise). Describe the issue in terms of Pipeworx tools/packs — don't paste the end-user's prompt. The team reads digests daily and signal directly affects roadmap. Rate-limited to 5 per identifier per day. Free; doesn't count against your tool-call quota.
| Name | Required | Description | Default |
|---|---|---|---|
| type | Yes | bug = something broke or returned wrong data. feature = a new tool or capability you wish existed. data_gap = data Pipeworx does not currently expose. praise = positive note. other = anything else. | |
| context | No | Optional structured context: which tool, pack, or vertical this relates to. | |
| message | Yes | Your feedback in plain text. Be specific (which tool, what error, what data was missing). 1-2 sentences typical, 2000 chars max. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses rate limit (5/day) and states it's free. Mentions not to include user prompt verbatim. No annotations exist, so description carries burden; sufficient for a feedback tool.
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 sentences front-loaded with purpose, then guidance, then constraints. No fluff, 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?
Covers purpose, usage context, and constraints (rate limit, content rules). No output schema, but return value is likely trivial. Adequate for a simple feedback 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?
100% schema coverage means parameters are well-described in schema. Description adds usage recommendations (be specific, 1-2 sentences) but does not significantly enhance parameter meaning beyond 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 'Send feedback to the Pipeworx team' with specific use cases (bug reports, feature requests, missing data, praise), differentiating it from siblings like ask_pipeworx.
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 when to use (feedback types) and provides content guidance (describe tools/data, avoid user prompt verbatim). Lacks explicit exclusion or alternative descriptions but is clear enough.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
recallAInspect
Retrieve a value previously saved via remember, or list all saved keys (omit the key argument). Use to look up context the agent stored earlier — the user's target ticker, an address, prior research notes — without re-deriving it from scratch. Scoped to your identifier (anonymous IP, BYO key hash, or account ID). Pair with remember to save, forget to delete.
| Name | Required | Description | Default |
|---|---|---|---|
| key | No | Memory key to retrieve (omit to list all keys) |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must fully disclose behavior. It identifies the tool as a read operation (retrieves memories) and states it works across sessions. However, it does not discuss potential limitations like memory size, persistence guarantees, or whether retrieval affects state (e.g., last access time). The description is truthful but minimal.
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 two sentences with no unnecessary words. The key action and two usage modes are front-loaded, making it easy for an agent to parse quickly.
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 retrieval tool with one optional parameter and no output schema, the description adequately covers what the tool does and how to use it. It mentions session persistence. However, it does not describe the return format (e.g., JSON structure) or behavior when key doesn't exist, but given the tool's simplicity, this is acceptable.
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 one parameter 'key' with a description already. The tool description adds value by explaining that omitting the key lists all memories and that memories persist across sessions. This goes beyond the schema to give the agent usage context.
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 ('Retrieve' or 'list') and the resource ('stored memory'). It distinguishes between retrieving a specific key and listing all, and the tool's function is distinct from sibling tools like 'remember' and 'forget'.
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 explains when to omit the key (to list all) and when to provide it (to retrieve a specific memory). It also mentions the context of use ('context you saved earlier'). However, it does not explicitly state when not to use this tool or name alternatives, though the distinction from siblings is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
recent_changesAInspect
What's new with a company in the last N days/months? Use when a user asks "what's happening with X?", "any updates on Y?", "what changed recently at Acme?", "brief me on what happened with Microsoft this quarter", "news on Apple this month", or you're monitoring for changes. Fans out to SEC EDGAR (recent filings), GDELT (news mentions in window), and USPTO (patents granted) in parallel. since accepts ISO date ("2026-04-01") or relative shorthand ("7d", "30d", "3m", "1y"). Returns structured changes + total_changes count + pipeworx:// citation URIs.
| Name | Required | Description | Default |
|---|---|---|---|
| type | Yes | Entity type. Only "company" supported today. | |
| since | Yes | Window start — ISO date ("2026-04-01") or relative ("7d", "30d", "3m", "1y"). Use "30d" or "1m" for typical monitoring. | |
| value | Yes | Ticker (e.g., "AAPL") or zero-padded CIK (e.g., "0000320193"). |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It discloses fan-out behavior, accepted date formats, and return structure. Does not mention side effects or safety, but tool is read-only.
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 sentences, front-loaded with core purpose, each sentence adds value. No wasteful content.
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 return format and usage scenarios. Lacks details on error handling, pagination, or result limits, but sufficient for typical use given sibling 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?
Schema coverage is 100% with descriptions. Description adds value by explaining relative date support and providing examples, and noting that type only supports 'company'.
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 'brief me on what happened with X' and the resource 'entity changes over time', and distinguishes from sibling tools by specifying fan-out to multiple sources.
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?
Explicit use cases: 'brief me on what happened with X' and change-monitoring workflows. Does not explicitly state when not to use or list 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.
rememberAInspect
Save data the agent will need to reuse later — across this conversation or across sessions. Use when you discover something worth carrying forward (a resolved ticker, a target address, a user preference, a research subject) so you don't have to look it up again. Stored as a key-value pair scoped by your identifier. Authenticated users get persistent memory; anonymous sessions retain memory for 24 hours. Pair with recall to retrieve later, forget to delete.
| Name | Required | Description | Default |
|---|---|---|---|
| key | Yes | Memory key (e.g., "subject_property", "target_ticker", "user_preference") | |
| value | Yes | Value to store (any text — findings, addresses, preferences, notes) |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It discloses persistence behavior: authenticated users get persistent memory, anonymous sessions last 24 hours. No mention of destructive behavior, but it's a safe write operation.
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 sentences, each purposeful: first defines action, second gives use cases, third adds persistence details. No wasted words, front-loaded with key 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?
Given no output schema and low complexity, description covers purpose, usage, and behavioral notes sufficiently for an agent to correctly select and invoke the 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?
Input schema has 100% coverage for both parameters. Description adds value by suggesting key naming conventions (e.g., 'subject_property') and noting that value can be any text, beyond what 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 'store' and the resource 'key-value pair in your session memory'. It distinguishes itself from siblings like 'forget' and 'recall' by focusing on the write operation.
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 lists use cases: saving intermediate findings, user preferences, or context across tool calls. It also notes persistence differences based on authentication, but does not provide explicit when-not-to-use or alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
resolve_entityAInspect
Look up the canonical/official identifier for a company or drug. Use when a user mentions a name and you need the CIK (for SEC), ticker (for stock data), RxCUI (for FDA), or LEI — the ID systems that other tools require as input. Examples: "Apple" → AAPL / CIK 0000320193, "Ozempic" → RxCUI 1991306 + ingredient + brand. Returns IDs plus pipeworx:// citation URIs. Use this BEFORE calling other tools that need official identifiers. Replaces 2–3 lookup calls.
| Name | Required | Description | Default |
|---|---|---|---|
| type | Yes | Entity type: "company" or "drug". | |
| value | Yes | For company: ticker (AAPL), CIK (0000320193), or name. For drug: brand or generic name (e.g., "ozempic", "metformin"). |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must fully disclose behavior. It explains the mapping and return of IDs and URIs, but lacks details on error handling, ambiguous inputs, or performance implications.
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 sentences, front-loaded with purpose, no redundant information. 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?
Given no output schema, the description adequately summarizes return values (IDs and URIs). It could mention possible failure modes, but overall it is complete for the tool's 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 100%, so baseline is 3. The description adds value by elaborating on the mapping for each entity type (SEC EDGAR, RxCUI) beyond the schema's short descriptions, aiding correct parameter usage.
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 resolves entities to canonical IDs for company and drug types, specifying the target identities (SEC EDGAR, RxCUI). It distinguishes from siblings like lookup_indicator by stating it replaces multiple lookup calls.
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?
Describes supported entity types and that it replaces 2-3 lookup calls, providing context for when to use. However, it does not explicitly state when not to use or mention alternatives among siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_pulsesAInspect
Search OTX threat-intel pulses by keyword. Returns pulse ID, name, description preview, tags, targeted countries, malware families, attack IDs, and indicator count.
| Name | Required | Description | Default |
|---|---|---|---|
| page | No | 1-based page (default 1) | |
| limit | No | 1-50 (default 20) | |
| query | Yes | Search term |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It lists return fields but omits behavioral traits like read-only nature, authentication needs, or rate limits. Adequate but not comprehensive.
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: first states purpose, second lists return fields. No wasted words and front-loaded with key action.
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?
The description lists return fields, compensating for lack of output schema. However, it could mention behavior for no results or pagination details beyond schema defaults.
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 already has 100% description coverage for all parameters. The description only adds the keyword context, providing no extra 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 'Search OTX threat-intel pulses by keyword' and lists specific return fields, distinguishing it from siblings like get_pulse (single pulse by ID) and lookup_indicator (indicator lookup).
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 usage when searching by keyword but does not explicitly state when not to use it or provide alternatives for other search types.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
validate_claimAInspect
Fact-check, verify, validate, or confirm/refute a natural-language factual claim or statement against authoritative sources. Use when an agent needs to check whether something a user said is true ("Is it true that…?", "Was X really…?", "Verify the claim that…", "Validate this statement…"). v1 supports company-financial claims (revenue, net income, cash position for public US companies) via SEC EDGAR + XBRL. Returns a verdict (confirmed / approximately_correct / refuted / inconclusive / unsupported), extracted structured form, actual value with pipeworx:// citation, and percent delta. Replaces 4–6 sequential calls (NL parsing → entity resolution → data lookup → numeric comparison).
| Name | Required | Description | Default |
|---|---|---|---|
| claim | Yes | Natural-language factual claim, e.g., "Apple's FY2024 revenue was $400 billion" or "Microsoft made about $100B in profit last year". |
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. It details the tool's behavior: supported sources (SEC EDGAR + XBRL), return values (verdict, structured form, actual value with citation, percent delta). It does not explicitly mention limitations (e.g., only US public companies, only specific financial metrics), which would enhance transparency.
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 two sentences, front-loaded with the core purpose. The second sentence adds valuable details without redundancy. Every part 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?
Given the tool's simplicity (one parameter, no output schema), the description is complete. It explains the supported domains, the return values, and the benefit over alternative approaches.
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 single parameter 'claim' has full schema coverage with a description and examples. The tool description adds context on how the claim is processed and what results to expect, which goes beyond the schema's basic description.
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's purpose: fact-check a natural-language claim against authoritative sources. It specifies supported claim types (company-financial for US public companies) and describes the return values (verdict, structured form, actual value with citation, delta). This distinguishes it from sibling tools like lookup_indicator or compare_entities, which have different purposes.
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 when to use the tool by stating it replaces 4–6 sequential agent calls, indicating it is a consolidated alternative. It also specifies supported claim types (company-financial). However, it does not explicitly mention when not to use or suggest alternative tools for unsupported claim types.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
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