Search and read Karandeep Singh's DevOps and AWS blog: articles and cheatsheets.
Average 4/5 across 11 of 11 tools scored. Lowest: 3.4/5.
Most tools have clear, distinct purposes: search, browse by tag, read, cheatsheets, related posts, and quiz. The only mild overlap is between browse_by_tag and search_posts, but their descriptions make the distinction clear. get_commands also overlaps slightly with get_cheatsheet and read_post, but it explicitly limits output to commands only.
Tool names follow a consistent verb_noun pattern throughout: list_*, get_*, search_posts, read_post, related_posts, random_post, quiz_me. The naming convention is uniform and predictable, making the tool set easy to navigate for an agent.
11 tools is well-scoped for a blog-focused server. Each tool serves a distinct content-access or discovery need without feeling bloated or redundant. The count feels appropriate for the domain.
The server covers the core blog workflow well: discovering topics, searching posts, browsing tags, reading full content, extracting commands, and finding related content. A minor gap is the lack of a simple 'list all posts' endpoint, but list_recent, browse_by_tag, and search_posts provide sufficient alternatives.
browse_by_tagBrowse by tagAList posts with a given tag (e.g. aws, lambda, cost, aurora). Use list_topics to see all tags.
| Name | Required | Description | Default |
|---|---|---|---|
| tag | Yes | A tag name. | |
| limit | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the description doesn't need to restate safety. It does not add behavioral details like pagination, ordering, or limit behavior, but it doesn't contradict 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 short sentences accomplish the task: the first defines the action and gives examples, the second routes users to list_topics for tags. No filler or redundancy, and key 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?
For a simple filtered read tool with annotations covering safety and no output schema, the description communicates the core behavior and points to the sibling for tag enumeration. The only real gap is not explaining the limit parameter, but that is optional and secondary to 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 description coverage is only 50%: tag is described but limit is not. The description adds useful example tag values but says nothing about limit's meaning, default, or constraints, so it fails to compensate for the undocumented optional parameter.
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 uses a specific verb and resource: 'List posts with a given tag'. It gives concrete examples (aws, lambda, cost, aurora) and clearly distinguishes this from sibling tools like search_posts or list_recent by the tag filtering criterion.
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 gives a clear usage context: use this tool when you need posts by tag. It also explicitly points to list_topics as the tool for seeing all tags, which helps route to an alternative. It doesn't enumerate when not to use it, but the guidance is sufficient for this simple tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_cheatsheetGet a cheatsheetAJump straight to a cheatsheet by topic (docker, cloudformation, cost, aurora, kubernetes, …) and get its full text.
| Name | Required | Description | Default |
|---|---|---|---|
| topic | Yes | Cheatsheet topic or name. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already establish read-only, idempotent, and non-destructive behavior. The description adds useful behavioral context by promising the full text content rather than a summary or list, and by giving example topic values. No contradiction with 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 sentence that front-loads the core action and output, with parenthetical examples that add practical guidance without bloat. 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 simple one-parameter read-only tool with no output schema, the description covers the invocation path, the expected return (full text), and the topic scope. Nothing essential is missing for an agent to call it correctly.
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 schema already documents the 'topic' parameter completely, so the baseline is 3. The description adds value by giving concrete topic examples (docker, cloudformation, cost, aurora, kubernetes, …) and framing the parameter as the direct access key for retrieving the cheatsheet.
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 states a clear action ('jump straight to'), a specific resource ('cheatsheet by topic'), and the expected output ('get its full text'). The topic examples and direct-access framing distinguish it from list/browse siblings like list_cheatsheets and browse_by_tag.
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 use case is implied: use this when you already know the topic and want the full cheatsheet text. However, it does not explicitly say when to use list_cheatsheets, list_topics, browse_by_tag, or get_commands instead, nor does it mention any exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_commandsJust the commandsAReturn just the runnable commands and code blocks from the best-matching article or cheatsheet — no prose. Use when the user wants the command, not the explanation.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max code blocks to return (default 6, max 12). | |
| topic | Yes | Topic or tool, e.g. docker, kubectl, terraform, psql. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare safe, idempotent read-only behavior. The description adds meaningful behavioral context: it returns only runnable commands and code blocks, excludes prose, and selects from a best-matching article or cheatsheet. It goes beyond the annotations without contradicting them.
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 short, front-loaded sentences carry all the key information: what is returned, what is excluded, and when to use the tool. There is no filler or redundant restating of the title.
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 only two parameters, a complete schema, and sufficient annotations, the description is largely complete. It could mention what happens if no matching article is found or clarify 'best-matching', but these are minor omissions given the low 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 description coverage is 100%, so the schema already fully documents 'topic' and 'limit' including default and maximum. The description adds no additional parameter-level meaning, so the baseline of 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 uses a specific verb ('Return') with a precise resource ('runnable commands and code blocks from the best-matching article or cheatsheet') and explicitly contrasts itself with prose. This clearly distinguishes it from siblings like read_post and get_cheatsheet.
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 gives an explicit usage condition: 'Use when the user wants the command, not the explanation.' This provides clear context, though it does not name an alternative tool to use when the user does want the explanation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_cheatsheetsList cheatsheetsAList all cheatsheets (quick-reference guides) on the blog.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the operation's safety profile. The description adds only the scope of returning all cheatsheets and does not describe return format or ordering, but for a simple list operation this is adequate.
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 one short sentence that states the operation and clarifies the resource type. Every word earns its place, and the parenthetical adds value without unnecessary length.
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 parameterless, read-only listing tool with strong annotations, the description sufficiently covers what the agent needs to invoke it. There is no output schema, but 'list all' reasonably implies a collection of cheatsheets; no critical invocation context is missing.
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 tool has zero parameters and schema description coverage is 100%, so no parameter explanation is needed. The baseline score of 4 applies because the description has no parametric burden to carry.
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 uses a specific verb ('List') and a clear resource ('cheatsheets'), and the parenthetical ('quick-reference guides') removes ambiguity. The word 'all' distinguishes this from singular tools like get_cheatsheet, so the agent can tell the operations apart.
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 clearly implies this tool is for enumerating every cheatsheet on the blog, with no filters or parameters. It does not explicitly name alternatives or exclusions, but the zero-parameter, list-all scope makes the intended usage clear enough.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_recentRecent postsBList the most recently published posts.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Default 8, cap 20. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds a recency/ordering trait ('most recently published'), but it does not disclose pagination behavior, return format, or what fields are included for each post.
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 filler. It communicates the action and the target resource directly, and every word 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?
For a simple list operation with one optional parameter and strong read-only annotations, the description is nearly adequate. It lacks an explicit statement of what a returned post contains or how 'recent' is defined, but these are minor gaps given the schema and annotations.
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 'limit' parameter, including its default and cap. The description does not add parameter-specific meaning, but the schema already fully documents what an agent needs.
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 uses a specific verb ('list') and resource ('recently published posts'), so the core purpose is clear. However, it does not explicitly distinguish itself from sibling tools like related_posts, search_posts, or read_post, though the resource scope is reasonably distinct.
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 guidance about when to use this tool versus alternatives such as search_posts or related_posts. The description implies a simple recent-posts listing use case, but it offers no exclusions, prerequisites, or routing context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_topicsList topicsAList every tag on the blog with a post count, for discovering what's covered.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds useful context about scope ('every tag') and output ('post count'), but does not disclose additional behavioral traits such as ordering or return format.
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 filler. Every word adds value, and the key output detail (post count) is included without unnecessary explanation.
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, zero-parameter, read-only tool with strong annotations, the description fully covers what the tool returns and why an agent would use it. No hidden complexity or missing essential context remains.
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 tool has zero parameters, so the baseline is 4. There are no parameter semantics for the description to clarify, and nothing is left ambiguous.
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 states a specific verb ('List'), a precise resource ('every tag on the blog'), and a distinguishing detail ('with a post count'). This makes it easy to tell apart from sibling tools like list_cheatsheets, list_recent, or search_posts.
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 phrase 'for discovering what's covered' gives clear context for when to use the tool. It does not explicitly name alternative tools or exclusions, but for a zero-parameter read-only list tool, the usage context is sufficient.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
quiz_meQuiz meATest the user's knowledge with real multiple-choice questions written for the blog's articles. Returns questions, options, and an answers section with explanations. Ask the questions first, then reveal the answers.
| Name | Required | Description | Default |
|---|---|---|---|
| count | No | How many questions (default 3, max 10). | |
| topic | No | Topic to be quizzed on, e.g. aurora, lambda, docker. Omit for a random quiz. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false. The description adds important behavioral context beyond the annotations: the tool returns questions and answers together, and the agent must ask questions before revealing the answers. This is a meaningful interaction constraint that is not captured by the structured metadata.
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: purpose, return shape, and the key usage rule. It is front-loaded with the main verb and avoids filler. Every sentence contributes essential 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?
For a read-only quiz tool, the description covers the core purpose, return contents, and the critical ask-before-reveal behavior. With annotations handling safety and the schema covering parameters, nothing essential is missing. The only minor gap is the lack of an explicit output format, but no output schema exists and the description is adequate.
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% coverage with clear descriptions for 'count' and 'topic.' The description adds no parameter-specific detail beyond what the schema already provides, so a baseline score of 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 states a specific verb and resource: 'Test the user's knowledge with real multiple-choice questions written for the blog's articles.' It also lists the return contents (questions, options, answers with explanations), which makes the tool's purpose concrete. None of the sibling tools overlap with quizzing, so it is clearly distinguishable.
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 an interaction guideline: 'Ask the questions first, then reveal the answers.' However, it does not explicitly state when to use quiz_me versus alternatives or name any exclusions. The intended use is implied by the content, but there is no direct guidance on selection or when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
random_postRandom postAReturn a random post from the blog — a surprise pick.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds no behavioral detail beyond the 'random' concept already present in the name, so there is minimal extra transparency value.
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, front-loaded sentence with no filler. The core behavior is stated first and the 'surprise pick' flourish is brief and harmless.
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 read-only tool with strong annotations, the description fully explains what the tool does. No output schema exists, but 'a post' is sufficient to convey the return concept for this simple 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?
The tool has zero parameters, so baseline is 4. The description correctly implies no inputs are needed and does not need to add parameter meaning.
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 states a specific verb and resource: 'Return a random post from the blog.' It clearly distinguishes this from sibling tools like read_post or related_posts by emphasizing the random/surprise nature.
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 phrase 'a surprise pick' implies a use case—when the user wants an unexpected post—but it does not explicitly contrast with alternatives or state when not to use it. No sibling exclusions are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
read_postRead an articleARead a post's full text by URL, paginated. Returns a window of characters; call again with the returned next offset to continue.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | Post URL or path, e.g. /posts/aurora-postgresql-setup-guide/. | |
| offset | No | Character offset to start from (default 0). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds meaningful behavioral context beyond annotations by explaining the paginated chunking mechanism and the need to use the returned next offset to continue reading. It does not cover edge cases like end-of-post handling, but the added pagination detail earns above-average credit.
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, tightly written sentence that front-loads the primary action, states the resource, and includes the pagination behavior. Every clause earns its place, with no filler or redundant phrasing.
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 read-only tool with two simple parameters and no nested objects, the description covers the essential calling pattern: provide a URL, optionally use offset, and iterate with the returned next offset. It does not mention how to detect the end of the post or the exact format of the returned window, but given the lack of an output schema and the tool's simplicity, the description is reasonably 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 description coverage is 100%, so the schema already documents url and offset. The description adds value beyond the schema by explaining the semantics of offset in the context of pagination ('Returns a window of characters; call again with the returned next offset to continue'), which clarifies how the offset parameter is meant to be used in practice.
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 states a specific verb ('Read'), a specific resource ('a post's full text by URL'), and a key behavioral trait (pagination). It clearly distinguishes itself from sibling tools like search_posts, random_post, and related_posts by requiring a URL and focusing on full-text retrieval rather than discovery or listing.
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 post URL and want to read its content. It also gives a pagination usage note ('call again with the returned next offset'), but it does not explicitly state when to prefer this tool over alternatives such as search_posts or related_posts, nor does it mention exclusions or prerequisites beyond having a URL.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_postsSearch the blogASearch Karandeep Singh's blog (DevOps/AWS articles and cheatsheets). Returns ranked cards with title, URL, tags, date, reading time, and summary.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max results (default 8, cap 20). | |
| query | Yes | Search terms, e.g. 'aurora failover' or 'lambda cold start'. | |
| section | No | Optional: restrict to 'posts' or 'cheatsheets'. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=false, so the safety profile is covered. The description adds useful behavioral detail about result ranking and the card fields returned. It does not discuss edge cases like empty results, ranking logic, or rate limits, but the annotations lower the burden here.
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 compact sentence that front-loads the tool's purpose and immediately follows with the return format. There is no redundant or filler content; every phrase 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?
For a search tool with three well-documented parameters and a rich annotation set, the description is quite complete. Since there is no output schema, describing the exact card fields is important and is done well. It could have added a bit more about ranking behavior or how to choose section, but the overall context is 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 description coverage is 100%, so the schema fully documents query, limit, and section. The description does not add parameter-specific meaning beyond what the schema provides, so the baseline score of 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 uses the specific verb 'Search' with a clear resource ('Karandeep Singh's blog') and scope ('DevOps/AWS articles and cheatsheets'). It also states what the tool returns ('ranked cards with title, URL, tags, date, reading time, and summary'), which clearly differentiates it from siblings like list_recent, browse_by_tag, and read_post.
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 gives clear context: this tool is for searching the blog by free-text query and can be restricted to posts or cheatsheets. However, it does not explicitly state when to prefer this over related alternatives like browse_by_tag or related_posts, nor does it list exclusions. The usage is clear but not exhaustive.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claiming proves that you control a remote MCP connector. It does not move, proxy, or interrupt the server.
Open the connector listing, choose Claim ownership, and sign in to Glama.
Complete one verification method:
GitHub identity — fastest for official registry listings. For a namespace such as io.github.alice/server, link the matching GitHub user or an account that owns the GitHub organization, then choose Claim with GitHub.
HTTP challenge — works when you can deploy a public file. Generate a token, publish the exact JSON Glama shows at /.well-known/glama.json on the same origin as the connector, then choose Check HTTP challenge.
DNS challenge — works when you control DNS but cannot change the server. Generate a token, create the exact TXT record Glama shows, wait for it to propagate, then choose Check DNS challenge.
After verification, Glama sends a confirmation email and gives you access to listing details, thumbnails, health checks, and analytics. Keep the HTTP file or DNS record in place: Glama periodically checks it and ownership remains verified while the token is discoverable.
The HTTP ownership file has this structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"claim": "glama_claim_..."
}Claim tokens are opaque, stable, and bound to the signed-in Glama account. They contain no email address or other personal information. If Glama can no longer discover a verified HTTP or DNS token, it starts a seven-day grace period before removing claim-based access. Restore the same token during that period to keep ownership verified. Never publish an email address, Glama session token, GitHub token, or connector credential as ownership proof.
If verification fails, confirm that you copied the current token exactly. The HTTP file must be public, return valid JSON with a successful HTTP response, and stay on the connector's origin. DNS changes may need more time to propagate. A claim cannot transfer to a different origin or hostname: if the connector target changes, Glama starts the grace period and the new target must be claimed separately after the previous claim is released.
For a connector linked to the official MCP Registry, registry updates continue to replace its name, description, and URL by default. After claiming, open Manage connector and enable Use Glama listing details as the source of truth if edits made on Glama should be preserved. Categories and thumbnails are always managed on Glama; registry linkage and technical connection settings continue to sync.
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
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For server owners:
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The server is experiencing an outage
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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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