Sidecar
Server Details
Cross-tool context for your data stack. Search, lineage, and impact across warehouse and BI tools.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
Glama MCP Gateway
Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.
Full call logging
Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.
Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.
Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Tool Definition Quality
Average 4.3/5 across 10 of 10 tools scored. Lowest: 3.3/5.
There are overlapping tool pairs: advanced_search vs resolve_asset_id (both find assets), calculate_downstream_impact vs inspect_lineage_details (both trace lineage), and describe_asset vs resolve_asset_id (both provide details). While descriptions help clarify distinct use cases, an agent could still be confused about which to call, especially for lineage and asset retrieval.
Nine of ten tools follow a verb_noun pattern (e.g., get_catalog_overview, describe_asset). The exception is 'advanced_search', which uses an adjective instead of a verb, breaking the pattern. This minor inconsistency does not significantly impair usability.
With 10 tools, the server is well-scoped for a data catalog. Each tool serves a distinct purpose in searching, describing, and analyzing data assets and lineage, without being overly numerous or sparse.
The tool set covers core catalog operations: asset search, lookup, detailed descriptions, lineage tracking, downstream impact analysis, and knowledge base access. Minor gaps exist, such as the lack of a full asset listing tool and absence of knowledge base search within files, but these are not critical for typical workflows.
Available Tools
10 toolsadvanced_searchAInspect
Search for data assets using identifier matching, keyword search, semantic similarity, or hybrid fusion.
Use 'identifier' mode (default) when you know the asset name or a pattern (supports * wildcards). Use 'keyword' when searching by domain terms or metadata keywords. Use 'semantic' for natural language queries about what the data represents. Use 'hybrid' to combine all approaches for the best recall.
Returns a ranked list of matching assets with identifiers, types, and relevance scores.
| Name | Required | Description | Default |
|---|---|---|---|
| mode | No | Search mode. 'identifier' (default): exact/wildcard match on asset_identifier. 'keyword': full-text search over indexed keywords. 'semantic': vector similarity search. 'hybrid': fuses identifier + keyword + semantic results via RRF. | identifier |
| tags | No | Filter to assets with any of these tags (OR semantics). | |
| limit | No | Maximum number of results (1-50). | |
| query | Yes | Search query. For identifier mode: asset name or wildcard pattern (e.g. 'fct_orders', 'stg_*', 'analytics.*customers'). For keyword/semantic/hybrid: natural language query (e.g. 'revenue metrics', 'user activity tracking'). | |
| tool_type | No | Filter to a specific tool. UPPERCASE, e.g. SNOWFLAKE, DBT_CLOUD, BIGQUERY. | |
| asset_type | No | Filter to a specific asset type. snake_case, e.g. snowflake_table, dbt_model. | |
| importance | No | Filter to a specific importance level: 'high', 'medium', or 'low'. |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
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 mentions the return format (ranked list with identifiers, types, relevance scores) but does not explicitly state read-only behavior, idempotency, or any side effects. This is adequate for a search tool but lacks explicit safety cues.
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 well-structured with bullet points for each mode, each sentence adds meaningful guidance, and there is no fluff or redundancy.
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 complexity (7 params, 4 modes) and the existence of an output schema, the description covers usage, mode selection, and return type. It lacks mention of pagination (limit but no offset) and sorting details, but overall it is sufficient for the intended search use case.
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 explaining when to use each mode and providing query examples for different modes, which goes beyond the schema's type and example descriptions.
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 searches for data assets using multiple modes, and provides distinct use cases for each mode. It differentiates itself from sibling tools by focusing on search functionality rather than impact analysis or lineage.
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 guides when to use each search mode (identifier for known names, keyword for domain terms, etc.), but does not mention when not to use this tool or suggest alternative tools for specific scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
calculate_downstream_impactAInspect
Find everything downstream of a data asset — tables, dashboards, and reports that would break or be affected if this asset changes.
Call this automatically after the user modifies any SQL file, dbt model, or source definition, or whenever they ask 'what depends on X?' or 'what breaks if I change Y?'
Returns a breakdown of affected assets by tool type and a full list of downstream nodes.
| Name | Required | Description | Default |
|---|---|---|---|
| max_depth | No | Downstream depth to traverse for impact analysis. | |
| tool_type | No | Optional tool/source hint (e.g., DBT_CLOUD, SNOWFLAKE). | |
| asset_identifier | Yes | Asset name or Sidecar asset identifier to analyze (e.g., 'fct_reviews' or 'model.jaffle_shop.fct_reviews'). |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Describes the output (breakdown by tool type and full list of downstream nodes) but does not disclose potential costs, permission requirements, or depth limits, which are minor omissions given the tool's read-only nature.
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 with no wasted words; purpose is front-loaded, usage guidance follows, and output summary completes. Highly 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 tool complexity (3 params, 1 required, output schema exists), the description covers purpose, usage, and output. It fits well among siblings and leaves no critical 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?
Schema coverage is 100%, so baseline is 3. The description adds examples for asset_identifier but does not significantly augment schema descriptions for the other parameters.
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 ('Find everything downstream') and resource ('data asset — tables, dashboards, and reports'), and it differentiates from sibling tools like get_column_lineage by focusing on impact analysis of changes.
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 provides when to call: automatically after modifications to SQL/dbt/source files, and in response to natural language queries. This covers automation and manual use cases.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
describe_assetAInspect
Fetch detailed information about a data asset by its identifier.
Resolves the asset, then fetches the requested sections in parallel. Use this after advanced_search or resolve_asset_id to get deeper information about a specific asset.
Sections:
overview: asset description, importance, owner, metadata
columns: full column schema with types and descriptions
tests: dbt test coverage (only for assets with a dbt parent)
context_counts: cheap count of linked tickets and Slack threads
tickets: full Jira/Linear ticket content
slack: full Slack thread content (truncated to first 5 messages per thread)
| Name | Required | Description | Default |
|---|---|---|---|
| include | Yes | Which sections to fetch. One or more of: 'overview' (description, importance, owner), 'columns' (schema), 'tests' (dbt test coverage), 'context_counts' (ticket/slack counts), 'tickets' (full ticket content), 'slack' (full slack thread content). Pick only what you need to keep payloads small. | |
| tool_type | No | Optional tool/source hint to disambiguate (e.g. SNOWFLAKE, DBT_CLOUD). | |
| asset_identifier | Yes | Asset name or Sidecar asset identifier (e.g. 'fct_orders' or 'model.jaffle_shop.fct_orders'). |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description fully covers behavior. Discloses parallelism in section fetching, truncation of Slack threads to 5 messages, and details each section's content. This is comprehensive behavioral disclosure.
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?
Well-organized: one-sentence summary, usage recommendation, then bullet list of sections. Every sentence adds value, no fluff. Efficient and clear.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Output schema exists, so no need for return value explanation. Description covers all necessary behavioral and parameter details, including parallelism and truncation. Complete for a tool with this 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%, baseline 3. Description adds value by listing include options with advice to pick only needed sections, gives examples for asset_identifier, and explains tool_type hint. Justification goes 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 'Fetch detailed information about a data asset by its identifier' with a specific verb and resource. It distinguishes from sibling tools like advanced_search and resolve_asset_id by specifying its role as a follow-up for deeper info.
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 recommends use after advanced_search or resolve_asset_id, providing clear context. Lacks explicit when-not-to-use or alternatives, but the sequence guidance is strong.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_catalog_overviewAInspect
Get a summary of everything available in this customer's Sidecar catalog.
Returns the connected tools (e.g. SNOWFLAKE, DBT_CLOUD, LOOKER), asset type breakdown with counts, all available tag names, and a customer-provided company summary describing the company's domain and data landscape (if set).
Call this at the start of any open-ended exploration (e.g. 'what data do we have?', 'show me all Snowflake tables'), or when a search returns no results and you need to understand what's actually in the catalog before trying again. Use the customer_summary field (when present) to understand the business context behind the data.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It clearly states it is a read-only operation returning a summary, with no mention of side effects. The return values are described in detail, providing 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 concise with two paragraphs: first states purpose and what is returned, second provides usage guidance. Every sentence adds value, 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?
Given no parameters, an existing output schema, and low complexity, the description fully covers what an agent needs. It explains the return values and when to use the tool, making it 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?
There are no parameters, so the baseline is 4. The description adds value beyond the empty schema by explaining the return values in detail, compensating for the lack of parameters.
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 a summary of the entire catalog, listing specific content (connected tools, asset breakdown, tags, company summary). It distinguishes from sibling tools by indicating it's for open-ended exploration or when search returns no results, unlike more targeted tools like describe_asset or advanced_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?
The description explicitly suggests calling at the start of exploration or after an empty search, providing clear context. While it doesn't explicitly name alternatives, the sibling tool names imply when to use other tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_column_lineageAInspect
Trace column-level lineage — which upstream columns feed into a column and which downstream columns consume it.
Use this when the user asks 'where does this column come from?', 'what columns feed into X.col?', or needs to understand column-level data flow.
Returns the target column, upstream columns (with levels), and downstream columns (with levels).
| Name | Required | Description | Default |
|---|---|---|---|
| tool_type | No | Optional tool/source hint (e.g., DBT_CLOUD, SNOWFLAKE) to disambiguate when multiple assets share the same identifier. | |
| column_name | Yes | The column name to trace lineage for (case-insensitive). | |
| upstream_depth | No | Levels of upstream column lineage to include. | |
| asset_identifier | Yes | Asset name or Sidecar asset identifier (e.g., 'fct_orders' or 'model.jaffle_shop.fct_orders'). | |
| downstream_depth | No | Levels of downstream column lineage to include. |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Describes the return structure (target column, upstream/downstream columns with levels). No annotations provided, but description implies read-only operation. No 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 concise paragraphs; every sentence adds value. Front-loaded with main purpose.
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 output schema exists, the description adequately covers purpose, usage, and behavior. Complete for the complexity of 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?
Schema coverage is 100%, so baseline is 3. Description adds context for the tool_type parameter about disambiguation, enhancing 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?
Clearly states the tool traces column-level lineage with specific examples of user queries. Distinguishes from siblings like describe_asset and inspect_lineage_details by focusing on column-level flow.
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 lists when to use the tool with concrete user questions. Does not explicitly mention when not to use it, but the context is clear enough for an agent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_context_repoBInspect
Return the clean OSI semantic contract currently deployed to this MCP token's Context Garage surface.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description bears full responsibility for behavioral traits. It only states that the tool returns a contract, but does not disclose any side effects, safety properties (read-only), or potential dependencies (e.g., token scope). This minimal information leaves the agent uncertain about impacts.
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, succinct sentence with no extraneous information. It conveys the core function efficiently.
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 no parameters and an output schema exists, the description is largely sufficient for basic understanding. However, the lack of context on the meaning of 'OSI semantic contract' and 'Context Garage surface' could hinder an agent's reasoning, slightly reducing completeness.
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?
There are zero parameters, so the input schema is fully covered. The description does not need to add parameter details. The baseline of 4 is appropriate as no additional semantic value is required.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool returns a specific resource ('clean OSI semantic contract') with a verb ('Return') and location. However, the domain-specific terminology ('OSI', 'Context Garage surface') may not be universally understood, slightly reducing clarity. It does not explicitly differentiate from sibling tools, but the unique resource likely sets it 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?
No guidance is provided on when to use this tool versus alternatives like 'get_catalog_overview' or 'describe_asset'. The description lacks context on appropriate scenarios or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_knowledge_base_file_contentAInspect
Fetch the full text content of a file from the customer's knowledge base.
Use this after calling list_knowledge_base_files to get file IDs. Works with any file in the knowledge base — playbooks, metric definitions, nuances, or captured context summaries.
| Name | Required | Description | Default |
|---|---|---|---|
| file_id | Yes | UUID of the knowledge base file to retrieve. Get file IDs from list_knowledge_base_files. |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, but description implies a read operation returning text content. Does not disclose potential errors, permissions, or format details. Moderate 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?
Two sentences, front-loaded with purpose, then usage context. No redundant words; 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?
With output schema present, description need not detail return format. Covers prerequisite (get file IDs) and file types. Adequate for a simple retrieval tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema covers the single parameter with full description and reference to list_knowledge_base_files. Tool description aligns but adds no new parameter semantics 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 the action 'Fetch the full text content of a file' and identifies the resource 'customer's knowledge base'. Distinguishes from sibling 'list_knowledge_base_files' by explaining the sequence of use.
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 advises to use after list_knowledge_base_files to get file IDs, and lists file types (playbooks, metrics, etc.) that are applicable. Lacks explicit exclusion scenarios but sufficient for basic usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
inspect_lineage_detailsAInspect
Trace the full data lineage for an asset — both upstream sources and downstream consumers.
Use this when the user asks 'where does this data come from?', 'what feeds into X?',
or needs the raw lineage graph. For pure downstream impact (blast radius), prefer
calculate_downstream_impact instead.
| Name | Required | Description | Default |
|---|---|---|---|
| tool_type | No | Optional tool/source hint (e.g., DBT_CLOUD, DBT_CORE). | |
| upstream_depth | No | Levels of upstream lineage to include. | |
| asset_identifier | Yes | Asset name or Sidecar asset identifier to trace (e.g., 'fct_reviews' or 'model.jaffle_shop.fct_reviews'). | |
| downstream_depth | No | Levels of downstream lineage to include. |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavioral traits. It mentions the tool returns a 'raw lineage graph' and covers both directions, but does not discuss performance, rate limits, or whether it modifies data (though it is read-only by nature). This is adequate but not exceptional.
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 concise: two sentences plus a usage tip. The main purpose is front-loaded, and every sentence adds value without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With an output schema present, the description does not need to explain return values. It covers the tool's purpose, usage guidance, and differentiation from siblings. All parameters are documented in the schema. The description feels complete for a lineage tracing tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline is 3. The description adds no extra meaning beyond what the schema already provides for parameters. It does not clarify or augment parameter semantics.
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 traces full data lineage (both upstream and downstream) and provides example queries. It distinguishes from the sibling tool 'calculate_downstream_impact' by contrasting use cases.
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 says when to use this tool (for 'where does this data come from?' queries) and when not to use it (prefer 'calculate_downstream_impact' for blast radius), including an alternative.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_knowledge_base_filesAInspect
List all files in the customer's knowledge base.
The knowledge base contains company-specific reference material — playbooks, metric definitions, business nuances, and conventions — uploaded by the customer's team.
Returns file names, paths, types, and IDs. Pass a file ID to get_knowledge_base_file_content
to read its content. Use the optional folder_path parameter to narrow results to a specific folder.
| Name | Required | Description | Default |
|---|---|---|---|
| folder_path | No | Optional folder path to filter results (e.g. '/Playbooks', '/Metrics and Drivers'). When omitted, returns all files across the entire knowledge base. |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided; the description discloses return fields (names, paths, types, IDs), mentions optional filtering, and implies no destructive behavior. It is transparent about what the tool does.
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 concise with three short paragraphs, each adding essential context. No fluff, 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?
Given the presence of an output schema, the description adequately covers purpose, filtering, return types, and linkage to a related tool. It is complete for a list tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with a detailed description for folder_path. The description repeats the filtering behavior but adds no new semantics beyond the schema, so 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 it lists knowledge base files, specifies what the knowledge base contains, and distinguishes from the sibling tool 'get_knowledge_base_file_content' by mentioning passing a file ID to read 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?
The description explains the tool's purpose, the optional filtering, and how to use it in conjunction with another tool. However, it does not explicitly state when not to use it or compare to other siblings beyond the content reader.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
resolve_asset_idAInspect
Look up any data asset by name across the full catalog (dbt, Snowflake, BigQuery, Looker, etc.).
Returns the asset's columns, description, owner, tags, and its Sidecar asset identifier
(asset_identifier field) — the fully-qualified internal name used by lineage and impact tools
(e.g. model.jaffle_shop.fct_orders, PROD_DB.ANALYTICS.FCT_ORDERS).
When a single asset is resolved, the response also includes a context_summary with
counts of linked tickets (Jira/Linear) and Slack threads. If those counts are non-zero
and relevant to the user's question, follow up with describe_asset using
include=['tickets'] or include=['slack'] to fetch the actual content.
Call this first whenever the user refers to a specific table, model, view, or dashboard.
| Name | Required | Description | Default |
|---|---|---|---|
| tool_type | No | Narrow to a specific tool. UPPERCASE, e.g. SNOWFLAKE, DBT_CLOUD, DBT_CORE, BIGQUERY, LOOKER, METABASE, FIVETRAN, POWER_BI, REDSHIFT. Call get_catalog_overview to see which tools are connected for this customer. | |
| asset_name | Yes | Name or query for the asset to look up. Plain name ('fct_reviews'), wildcard ('fct_*'), or filters inline ('fct_reviews tool_type:SNOWFLAKE', 'tag:finance'). Filter keys: tool_type (UPPERCASE, e.g. SNOWFLAKE, DBT_CLOUD, BIGQUERY), asset_type (snake_case, e.g. snowflake_table, dbt_model), tag (exact tag name). Call get_catalog_overview to see all valid values for this customer. | |
| asset_type | No | Narrow to a specific asset type. snake_case, e.g. snowflake_table, dbt_model, dbt_source, looker_dashboard. Call get_catalog_overview to see all asset types present for this customer. |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description carries the burden. It describes the return fields (columns, description, owner, tags, asset_identifier) and mentions the context_summary for single assets. It does not cover potential destructive behavior or rate limits, but the behavior is clearly 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?
The description is well-structured with the main purpose upfront, followed by return details and usage guidance. It is slightly long but each sentence adds value. Could be trimmed minimally.
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 complexity of the tool (3 parameters, output schema exists), the description is complete. It covers all necessary aspects: what it does, what it returns, how to use parameters, and interaction with 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 description coverage is 100%, and the description adds significant value beyond the schema. It provides examples for asset_name (plain name, wildcard, filters), explains filter keys, and instructs to call get_catalog_overview for valid tool_type and asset_type values.
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: 'Look up any data asset by name across the full catalog'. It specifies the verb 'look up' and the resource 'data asset', and distinguishes from siblings like 'describe_asset' and 'advanced_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?
Explicit guidance is provided: 'Call this first whenever the user refers to a specific table, model, view, or dashboard.' It also instructs to follow up with 'describe_asset' for detailed info, and mentions 'get_catalog_overview' for valid values.
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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