CEOInterviews.AI: C-Suite Intelligence for AI Agents
Server Details
Connect your AI agent to 20,000+ executives (CEO, CFO, COO) of all major companies, 1M+ verified quotes, and full interview transcripts. S&P 500, NASDAQ, AI startups, Federal Reserve officials. 4 MCP tools, pay only for what you use. $5/1,000 results, no minimum, no commitment, cancel anytime. You only pay for returned valid results. You must create an API key at https://mcp.ceointerviews.ai and then authenticate here using header Authorization: Bearer <token> For access to our full data API please access https://ceointerviews.ai
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- Streamable HTTP
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Tool Definition Quality
Average 3.9/5 across 4 of 4 tools scored. Lowest: 3.3/5.
Each tool serves a distinct layer of the intelligence workflow: entity discovery (companies vs executives) and content retrieval (quotes vs full transcripts). No functional overlap exists between the four tools, and search_executives explicitly clarifies its relationship to the other tools.
All tools follow a consistent verb_noun snake_case pattern. 'Search' is used for discovery operations (companies, executives, quotes) while 'get' is used for document retrieval (transcripts), accurately reflecting distinct operation types.
Four tools is slightly minimal but reasonable for this focused domain. Each tool earns its place in the intelligence-gathering workflow, though additional tools for date filtering, trending topics, or direct ID-based lookup would strengthen the surface.
Core read operations for the intelligence domain are covered: entity discovery and content retrieval. Minor gaps include no direct get-by-ID endpoints for companies/executives (relying solely on fuzzy search) and no metadata tools for browsing available sources, but the essential transcript→quote→entity linkage is complete.
Available Tools
4 toolsget_transcriptsAInspect
PRIMARY research tool — returns full verified interview transcripts from executive media appearances including interviews, podcasts, earnings calls, and conferences. Use this tool FIRST for any question about what an executive has said, their views, opinions, strategy, or commentary on any topic. Transcripts contain rich, detailed context far beyond what short quotes provide. Use a small page_size (5-7) to avoid excessive token usage. Keyword search requires an entity or company filter.
| Name | Required | Description | Default |
|---|---|---|---|
| keyword | No | Search within transcript text (requires entity or company filter) | |
| page_num | No | Page number (default 1) | |
| entity_id | No | Exact entity ID from search_executives | |
| page_size | No | Results per page, 1-500 (default 5). Keep small (5-7) because transcripts are large and token-heavy. | |
| company_id | No | Exact company ID | |
| entity_name | No | Executive name, fuzzy matched (e.g. 'Tim Cook') | |
| company_name | No | Company name or ticker, fuzzy matched (e.g. 'Apple', 'TSLA') | |
| filter_after_dt | No | Only items after this date (ISO 8601, e.g. '2024-01-01') | |
| filter_before_dt | No | Only items before this date (ISO 8601) |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, description carries full disclosure burden. Adds valuable context about 'verified' data quality and specific coverage types. However, fails to explicitly confirm read-only status, rate limits, or whether results are real-time versus cached.
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 tight sentences with zero waste: purpose front-loaded in sentence 1, coverage scope in sentence 2, critical usage constraint in sentence 3. Every sentence earns its place with appropriate density for the 9-parameter complexity.
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 9 parameters with complex interdependencies and existing output schema, description adequately covers the essential business rule (keyword requires entity/company filter) and data scope. Could enhance by clarifying the lookup dependency on search_executives for entity_id values, but sufficient for correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, establishing baseline 3. Description restates the keyword filter constraint already present in the schema's keyword parameter description, but does not add semantic information regarding fuzzy matching behavior, ID resolution workflows, or date range logic beyond schema documentation.
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?
Clear specific verb 'Get' with resource 'verified interview transcripts' and scope 'from executive media appearances' including specific types (podcasts, earnings calls). Distinguishes from siblings search_companies/search_executives/search_quotes by focusing on transcript retrieval versus entity discovery or quote extraction.
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?
States critical constraint that 'Keyword search requires an entity or company filter', preventing invalid invocations. However, lacks explicit guidance on when to use this versus sibling search_quotes, or prerequisite workflow using search_executives to obtain valid entity_id values.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_companiesBInspect
Search companies tracked in the CEOInterviews database. Returns company name, ticker, index membership, and classification flags.
| Name | Required | Description | Default |
|---|---|---|---|
| keyword | No | Search by company name or stock ticker | |
| page_num | No | Page number (default 1) | |
| is_nasdaq | No | Only NASDAQ-listed companies | |
| is_snp500 | No | Only S&P 500 companies | |
| page_size | No | Results per page, 1-500 (default 10) | |
| is_nasdaq100 | No | Only NASDAQ 100 companies | |
| is_usa_based | No | Only US-based companies | |
| is_ai_startup | No | Only AI startups | |
| is_china_based | No | Only China-based companies | |
| is_top_startup | No | Only top startups | |
| is_europe_based | No | Only Europe-based companies |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description mentions the specific database source (CEOInterviews) which adds context. It also lists return fields, though this is redundant since an output schema exists. It lacks details on search behavior (case sensitivity, partial matching, AND/OR logic between filters) or rate limits.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description consists of exactly two efficient sentences with zero waste. The first sentence establishes purpose and the second specifies return values, presenting information in a front-loaded manner.
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 11 parameters (all optional with boolean/null filters), pagination controls, and an existing output schema, the description is minimally adequate. It could benefit from mentioning that all parameters are optional or explaining how multiple filters interact (e.g., cumulative AND logic).
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 100% description coverage, documenting all 11 parameters (filters like is_nasdaq, is_snp500, etc.). Since the schema fully explains the parameters, the description doesn't need to add parameter details, meeting the baseline score of 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 verb (Search), resource (companies), and scope (CEOInterviews database), providing specific context about the data source. However, it does not explicitly distinguish when to use this versus sibling tools like 'search_executives' or 'search_quotes'.
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 alternatives, nor does it mention prerequisites or conditions for use. There is no mention of the sibling tools or filtering strategies.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_executivesAInspect
Search 20,000+ tracked executives, CEOs, politicians, and leaders. Returns name, title, institution, bio, and company metadata. Use this to find entity IDs for use with get_transcripts and search_quotes. The keyword param fuzzy-matches against name, title, company name, and ticker — you can combine terms in any order (e.g. 'devinder kumar amd', 'cook apple ceo').
| Name | Required | Description | Default |
|---|---|---|---|
| gender | No | Filter by gender: M, F, or O | |
| keyword | No | Fuzzy search across executive name, title, company name, and ticker. Supports partial names, tickers, titles, and multi-term queries in any order. Examples: 'Tim Cook', 'Elon', 'AAPL', 'CEO Tesla', 'devinder kumar amd' | |
| page_num | No | Page number (default 1) | |
| is_nasdaq | No | Only NASDAQ-listed company executives | |
| is_snp500 | No | Only S&P 500 company executives | |
| page_size | No | Results per page, 1-500 (default 10) | |
| company_name | No | Filter by company name or ticker (e.g. 'Apple', 'AAPL') | |
| is_nasdaq100 | No | Only NASDAQ 100 company executives | |
| is_usa_based | No | Only US-based company executives | |
| is_ai_startup | No | Only AI startup executives | |
| is_china_based | No | Only China-based company executives | |
| is_top_startup | No | Only top startup executives | |
| is_europe_based | No | Only Europe-based company executives |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It successfully discloses fuzzy-matching behavior across multiple fields and flexible term ordering. Could be improved by mentioning sorting behavior or result relevance algorithm, but covers core behavioral trait well.
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?
Four well-structured sentences with zero waste. Front-loads scope (20,000+), mid-section explains returns and workflow, end details critical parameter semantics. Every sentence 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 13 optional parameters fully documented in schema and output schema available, description appropriately focuses on workflow context and search semantics rather than repeating structured data. Mentions the specific entity ID purpose linking to sibling tools.
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%, establishing baseline 3. Description adds significant value by explaining the keyword parameter's fuzzy-match semantics and demonstrating multi-term query syntax ('devinder kumar amd', 'cook apple ceo') beyond what the schema captures.
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?
Excellent specificity: states exact resource (20,000+ executives, CEOs, politicians), action (search), and scope. Distinguishes from sibling search_companies by explicitly targeting people rather than organizations.
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 workflow guidance: 'Use this to find entity IDs for use with get_transcripts and search_quotes' clearly defines when to invoke this tool versus its siblings, establishing the dependency chain.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_quotesAInspect
SUPPLEMENTARY tool — search short notable quotes from executive media appearances. Each quote includes who said it, when, where, and source context. Only use this when the user specifically asks for brief quotations or exact wording. For general research about what an executive has said or thinks, use get_transcripts instead — it provides much richer context.
| Name | Required | Description | Default |
|---|---|---|---|
| keyword | No | Search within quote text | |
| page_num | No | Page number (default 1) | |
| entity_id | No | Exact entity ID | |
| page_size | No | Results per page, 1-500 (default 10) | |
| company_id | No | Exact company ID | |
| is_notable | No | Only notable/important quotes | |
| entity_name | No | Executive name, fuzzy matched | |
| company_name | No | Company name or ticker, fuzzy matched | |
| filter_after_dt | No | Only quotes after this date (ISO 8601) | |
| filter_before_dt | No | Only quotes before this date (ISO 8601) | |
| is_controversial | No | Only controversial quotes | |
| is_financial_policy | No | Only financial/policy-related quotes |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full disclosure burden. It adds valuable context about result richness ('who said it, when, where, and full source context') and corpus scope, but omits operational traits like read-only safety, pagination behavior, or rate limits that agents need for invocation planning.
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 two-sentence structure is optimally front-loaded: the first declares the core capability (searching the quote corpus), while the second clarifies data completeness (source context). Every word advances understanding without repetition.
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 existing output schema and 12 well-documented optional parameters, the description adequately covers the tool's scope and return value characteristics. It could be strengthened by noting the flexible/all-optional parameter design, but remains sufficient for selection.
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%, with detailed parameter documentation like 'Executive name, fuzzy matched' and 'Only quotes after this date (ISO 8601)'. The description correctly relies on the schema for parameter semantics, meeting the baseline expectation without redundancy.
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 opening sentence uses a specific verb ('Search') with a well-defined resource ('1,000,000+ notable quotes from executive media appearances'), clearly distinguishing it from sibling tools that handle transcripts, companies, or executives rather than quotes.
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?
While the description lacks explicit when-to-use guidance or named alternatives, the resource type ('quotes') implicitly signals to use this for searching specific statements versus get_transcripts for full context or search_companies for organizational data.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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