Flevy
Server Details
Search 10,000+ consulting frameworks, templates, financial models, and management case studies
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Tool Definition Quality
Average 4.7/5 across 5 of 5 tools scored.
Each tool has a clearly distinct purpose: search_content for searching, list_topics for listing topics, get_topic_details for deep topic info, get_content_details for item metadata, and get_slide_deep_dive for slide previews. No overlapping functionality.
All tool names follow verb_noun snake_case pattern consistently: get_content_details, get_slide_deep_dive, get_topic_details, list_topics, search_content. Verbs 'get', 'list', 'search' and nouns clearly indicate the resource.
Five tools are well-suited for this server's purpose: a search tool, a topic listing, a topic detail, an item detail, and a slide preview. Not too few or too many.
The tool set covers the full discovery lifecycle: searching with filters, exploring topics, retrieving full metadata, and previewing slides. No obvious gaps for a catalog exploration server.
Available Tools
5 toolsget_content_detailsGet Content DetailsARead-onlyInspect
Full metadata for one Flevy item, by content_id from search_content (e.g. "doc-1234" or "case-567"). Documents return the author with their credentials (headline, bio, LinkedIn, profile URL; pass the author name to search_content's author filter to list more of their documents), full description, editor summary, AI summary, and editorial review when available, page/slide count, price, FlevyPro inclusion, management topics, ranking badge, and the number of slide deep dives available. Case studies return the client situation, TL;DR, and summary. Call this before recommending an item so you can describe it accurately and cite the author's credentials, and share the returned flevy.com URL.
| Name | Required | Description | Default |
|---|---|---|---|
| content_id | Yes | The content_id from search_content: "doc-<n>" for documents, "case-<n>" for case studies. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only and non-destructive behavior. The description adds context on what data is returned, such as author credentials and URL, which is useful 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?
The description is front-loaded with the core purpose. While somewhat long, it efficiently lists many details without redundancy. Could be slightly more concise but still effective.
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 thoroughly explains return values for both document and case study types, including author credentials, summaries, counts, and URL. It fully covers the tool's output.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema coverage, the description adds value by explaining content_id format with examples (e.g., 'doc-1234') and linking to search_content, going beyond the schema's minimal 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 it retrieves full metadata for one Flevy item, using content_id from search_content. It distinguishes from sibling tools by specifying the data returned and referencing search_content.
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 instructs to call before recommending an item for accurate description and author credential citation. Also advises passing author name to search_content filter for more documents, providing clear usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_slide_deep_diveGet Slide Deep DiveARead-onlyInspect
Slide-by-slide preview of a Flevy document (a "doc-" content_id). Returns every showcased slide with its name, a text description of what the slide contains (you cannot see the image, so use the description), a preview image URL, and a deep link to that slide on flevy.com. Use when a user wants to know what is inside a specific presentation before purchasing, or to reference an individual slide. Only some documents have deep dives; get_content_details reports the count.
| Name | Required | Description | Default |
|---|---|---|---|
| content_id | Yes | A document content_id from search_content, e.g. "doc-1234". |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, so safety is clear. Description adds useful context: the user cannot see the image, so descriptions are provided, and it outlines the return structure (name, text description, preview URL, deep link).
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 that efficiently convey purpose, usage, and limitations. No fluff, but could be even more compact by combining the last two sentences.
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 tool with one parameter and no output schema, the description adequately explains the output content and provides necessary context about availability. The lack of output schema is mitigated by the detailed description of returned fields.
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. The description adds value by specifying the format ('doc-<n>') and origin ('from search_content'), which aids correct invocation beyond the schema alone.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states it returns a slide-by-slide preview of a Flevy document, specifying the content_id format and distinguishing it from siblings by mentioning that only some documents have deep dives and that get_content_details reports the count.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly states when to use: 'when a user wants to know what is inside a specific presentation before purchasing, or to reference an individual slide.' Also notes the limitation that not all documents support deep dives, directing users to check get_content_details for availability.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_topic_detailsGet Management Topic OverviewARead-onlyInspect
An end-to-end overview of one management topic: its definition, an in-depth explanation of the discipline, the 3 editor-curated top documents, all known aliases, document and case study counts, related topics, and the topic page URL. Use this to survey a discipline before going deep — e.g. "what does Digital Transformation cover and what are its key frameworks" — or to orient when the user describes a broad problem area. Follow with search_content (topic filter) for the full catalog.
| Name | Required | Description | Default |
|---|---|---|---|
| topic | Yes | The management topic or any of its aliases, e.g. "Digital Transformation" (see list_topics). |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint true, so description adds value by listing exact return fields (definition, explanation, top documents, aliases, counts, related topics, URL). No contradictions. Discloses more than just 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 that front-load purpose, then detail components and usage. Every sentence is meaningful; 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 tool with one parameter and no output schema, the description adequately specifies return fields. Mentions sibling tool usage. Could add example or note about case sensitivity, but overall complete.
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 a description for the single 'topic' parameter. Description enriches by noting it accepts aliases and references list_topics for known topics, adding context 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?
Clearly states it provides an 'end-to-end overview of one management topic' with specific components (definition, explanation, top documents, aliases, counts, related topics, URL). Distinguishes from siblings by focusing on overview vs. deep dive or 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?
Explicitly says 'Use this to survey a discipline before going deep' and suggests follow-up with search_content. Provides clear when-to-use and alternative guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_topicsList Management TopicsARead-onlyInspect
The canonical list of management topics Flevy's catalog is organized under, each with its known aliases (e.g. "Digital Transformation" and "Digital Transformation Strategy" may be the same topic) and content counts. Use this to map a user's phrasing to the exact topic filter accepted by search_content, or to show what subject areas Flevy covers.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds context beyond annotations (readOnlyHint, destructiveHint) by detailing the output includes aliases and content counts. No hidden behaviors or contradictions.
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, front-loaded with the core purpose. Every sentence adds essential information without redundancy or fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given zero parameters and no output schema, the description fully covers what the tool does and how to use it. The context of siblings and annotations leaves no gaps.
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 (0 params), so baseline is 4. The description enhances understanding by specifying what the returned data contains (topics, aliases, counts), adding value over 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 it returns the canonical list of management topics with aliases and content counts. It distinguishes itself from siblings like get_topic_details and search_content by specifying its role in mapping user phrasing to filters.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly states when to use: to map user phrasing to a topic filter for search_content, or to show covered subject areas. This implies alternatives like get_topic_details for single topic details, providing clear usage guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_contentSearch Flevy ContentARead-onlyInspect
Search Flevy's marketplace of consulting frameworks, PowerPoint templates, Excel financial models, business toolkits, and management case studies. Use this whenever a user needs a best-practice framework, methodology, template, financial model, or real-world case example on any business or management topic (strategy, digital transformation, supply chain, pricing, operational excellence, M&A, etc.). Returns up to 10 relevance-ranked results across two content types: "document" (premium documents authored by management consultants) and "case_study" (management case studies). Each result carries a content_id for get_content_details. Filters: topic (single, or "topics" for documents covering ALL of several topics), author (list more documents from an author seen in results), filetype (including tier1_consulting_deck for McKinsey-style strategy decks), content_type. Topic-filtered responses also list related_topics to pivot to. Provide at least one of query, topic(s), or author; use list_topics to map user phrasing to a canonical topic.
| Name | Required | Description | Default |
|---|---|---|---|
| query | No | Free-text search, e.g. "balanced scorecard", "post-merger integration checklist", "DCF model". Optional when topic or author is provided (returns their top content). | |
| topic | No | Optional management topic filter, e.g. "Digital Transformation". Accepts any known alias (see list_topics). | |
| author | No | Optional author filter — the author name exactly as shown in document results. Use to list other documents from the same author, alone or combined with a topic. Documents only. | |
| topics | No | Optional: up to 3 topics; returns documents associated with ALL of them (intersection), e.g. ["Digital Transformation","Supply Chain Analysis"]. Documents only. | |
| filetype | No | Optional file-type filter (documents only). Rules of thumb: frameworks, methodologies, and presentation templates are powerpoint; financial models are excel; tier1_consulting_deck = presentations crafted in the style of tier-1 consulting firms (McKinsey/MBB) with the headline-body-bumper slide structure — use it when the user wants consulting-grade frameworks or decks; flevypro = documents included with a FlevyPro subscription. | |
| content_type | No | Optional filter. Default: both types, documents first. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare readOnlyHint=true and destructiveHint=false, indicating a safe read operation. The description adds that results are relevance-ranked, limited to 10, and include two content types with content_id for downstream use, providing valuable behavioral context beyond annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is densely packed with essential information in a single paragraph. It is well-structured starting with purpose, then usage, then result details and filters. Some repetition with schema descriptions could be trimmed, but overall 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?
Given the tool has 6 parameters and no output schema, the description adequately covers usage, result format (content types, fields like content_id), and links to sibling tools. Missing details on error handling or empty results, but acceptable 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?
While the schema covers all parameters (100%), the description adds practical guidance, e.g., explaining when to use filetype=tier1_consulting_deck and that topic-filtered responses list related_topics. This enriches the agent's understanding beyond the schema definitions.
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 tool searches Flevy's marketplace for consulting frameworks, templates, models, and case studies. It differentiates from sibling tools by noting that results carry content_id for get_content_details and advises using list_topics for topic mapping.
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?
Provides clear guidance: 'Use this whenever a user needs a best-practice framework...' and suggests alternatives like list_topics for mapping user phrasing. Also explains when to use specific filters like filetype tier1_consulting_deck.
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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